• 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.

Metabolomics, with its ability to track the behavior of small molecules, has a unique place in the 5P transformation, providing a measure of the impact of not only genetics, but also environment and lifestyle:

• Metabolomics enables preventive medicine through early, often reversible metabolic shifts that precede the onset of chronic disease
• Metabolomics enables predictive medicine through robust biomarker signatures already harnessed to foretell drug response
• Metabolomics enables precision medicine through stratification and metabotypes that group patients based on the molecular presentation of their disease,
• Metabolomics enables population based medicine by providing insight into environmental variance necessary to step into the post-genomic era
• Metabolomics enables participatory medicine through patient centric care including remote at-home sampling and actionable biochemical information.

This multifaceted perspective provided by metabolomics remains to be included in the routine toolkit of most medical practitioners, but when it does, it will drive a massive transformation in the way that we approach health.

Driving the adoption of metabolomics through 5P medicine

Here I introduce a simple but powerful model called Screen | Leverage | Translate. This 3-step framework explains how any scientist can harness the information contained in metabolomics and turn it into the solution to their specific 5P medicine problem.

If you are familiar with my book, the STORY principle, you’ll notice that Screen | Leverage | Translate follows a similar direction to the principle I describe there, planning an experiment, executing it, and excavating the gems out of your dataset before carving it into the answer you need for your research. Screen | Leverage | Translate is a streamlined version of the STORY principle, adapted to specific applications in 5P medicine.

Step 1 | Screen

This is the data acquisition step, where you’ll analyze the samples required for your experiment. Going back to the STORY principle, there is an unspoken understanding that by the time you reach sample measurement, you’ve already put in the work to determine your research question and how metabolomics will help answer it.

This doesn’t mean you know the results, rather that you’ve accounted for any factors that may predictably confound your findings. With a measure as sensitive to the environment as metabolomics, it is crucial to have these factors in check, in order to make the most of each study. Today, enough is known about these confounders to help you avoid interference by, e.g. sex, age, fasting time, diet, exposures, and more. If unsure, you can always refer back to the Safety Check step in my book or ask your metabolomics collaborators for support in planning your experiment.

A few questions to ask in preparation for this screening step relate to the method used for metabolomics measurement. Every method has benefits and limitations. Understanding the form of answer you need to your research question will turn certain benefits into requirements, and others into “nice-to-haves”. The method that provides your requirements without hindering the leveraging of the results is the one best suited for your study.

In our pathway spotlight below, I will describe an example focused on polyamine metabolites and their role in immunotherapy response.

Step 2 | Leverage

During your preparation step, you’ll also have planned the tools to be used to analyze and leverage your data. These days, with data and study design complexity increasing, the more automated the tool, the better.

Among omics, metabolomics is unique in the way that its datapoints connect to each other and to health. Unlike other omics, no direct line can be drawn between a given metabolite and a gene of origin. This makes the application of genomics-derived tools impossible, which is why the metabolomics community has had to develop its own data analysis tools.

Metabolites are most often grouped and analyzed around metabolic pathways. This work demands a deep knowledge of these mechanisms, that is condensed in databases like Wikipathways, Reactome and KEGG; databases that are constantly growing, as our knowledge of metabolism in different contexts expands.

Context is crucial.

Depending on the matrix you are studying (blood, urine, feces, tissue from an organ, cell model…), different metabolic pathways are at work – interacting, influencing each other, balancing each other.

The optimal way to leverage metabolomics is intricately linked to the context of the study. Species, matrix, disease context, study design – when combined, these factors make each experiment unique. But knowledge from previous experiments is a great way to start leveraging your own. I’ll introduce the one we have developed in our software in our pathway spotlight example.

Step 3 | Translate

Translation is where every omic meets its end application; where the well-oiled machine of data acquisition needs to slow down and level with what awaits it on the other side.

For applications where a routine solution is the expected outcome, translation is just the creative part before another well-oiled machine is created; e.g. a biomarker panel for routine disease screening, a biomarker for drug response prediction, a bioinformatics pipeline combining metabolomics with other omics and medical endpoints.

Seen through this lens, translating metabolomics is the thrilling part. It is the moment of truth, where the results of the previous steps are put to the test: will they hold up in a follow-up study? Because indeed, after crafting the solution to your original research question, you’ll need to verify its power in one or more validation studies.

This is when research begins to bring value to society; how we truly leverage knowledge to improve lives. Three years after its publication, I still refer to the paper by Tintelnot et al. on pancreatic cancer patient response to chemotherapy. Using metabolomics, this team identified a microbial metabolite, 3-indole acetic acid (3-IAA), as higher at baseline in patients who responded to treatment. Many studies end there, but not this time. What followed was a set of experiments in an animal model of pancreatic cancer, testing the impact of the patients’ microbiomes in fecal matter transplantation (FMT) experiments, an evaluation of the effect of direct supplementation with 3-IAA and other metabolites, and painting the picture of how 3-IAA modulated the immune system of the mice to improve chemotherapy response.

These are the kinds of studies I love to read: where omics are leveraged to start crafting the solutions to a specific problem. This is why translation is called “translation”. It takes work and creativity to transform the tabular results provided by omics into the life-sized solutions needed in the clinics.

In the pathway spotlight below, I explore how metabolites, specifically polyamines, may bring similar solutions for immunotherapy response. My goal here is to walk you through bits of published science to understand how you could leverage and translate your next metabolomics experiment to accelerate its adoption in the future medical landscape.

Pathway spotlight | Polyamine acetylation as a driver of immunotherapy response

Metabolomics profiling creates a broad picture of the metabolome. Depending on the level of expertise of scientists, they may enter this step with a fully blank mind or with an idea of what they think they will see. This second case is ideal, as it can also use some of the results as a form of positive control for the phenotypes that they are studying.
Here we will work under the assumption that broad metabolic profiling identified differences in polyamines in a cohort of patients receiving immunotherapy treatment for cancer. Since this pathway is relevant in several types of cancer, I will not specify here and rather detail the disease context each time that a study is discussed in this spotlight

Step 1 | Screen
In a study of patients undergoing anti CD19 CAR T-cell therapy for relapsed or refractory large B cell lymphoma, Fahrmann et al. found that acetylated polyamines, specifically acetylspermidine and diacetylspermidine, were elevated in non‑responders and strongly linked to poor treatment durability (Fahrmann et al. 2022).
The first thing any scientist would do is to research what these metabolites are known for.

Polyamines – putrescine, spermidine, and spermine – are essential molecules for cell growth, gene regulation, and immune function. Cancer cells exploit this pathway aggressively. A key regulator of this system is spermidine/spermine N¹ acetyltransferase (SSAT), the enzyme responsible for polyamine acetylation and export.

Mechanistic investigations show that overexpression of SSAT dramatically increases flux through the polyamine pathway, triggering both acetylation and compensatory upregulation of synthesis. This “futile cycle” results in a large, constantly replenished polyamine pool (Kramer et al. 2008). In addition, acetylated polyamines accumulate in several cancers, including breast, prostate, and lung tumors, contributing to an immunosuppressive microenvironment that blunts therapeutic efficacy. (Thomas & Thomas, 2007, Kramer et al. 2008)

This information is valuable and suggests that these metabolic differences may link to differences in treatment response. However, literature research can be a long and confusing endeavor. This is why the whole community strives to automate leveraging and interpreting metabolomics results. In the following step, we’ll review how MetaboINDICATOR can facilitate the first steps of this work.

Step 2 | Leverage
Enough data has been generated by now to inform on the expected metabolic profile of at least part of an experiment and better understand its context. This is what MetaboINDICATOR was designed for. This module of the WebIDQ software contains hundreds of sums and ratios of metabolites hand-picked from the literature and distributed across disease- and mechanism-related categories.
The most open-ended way to use this module is to compute any indicator for which the metabolites are quantified in the sample. This is my recommendation to maximize the chance to learn from other fields; although it is also possible to select only the groups of indicators of interest to the study, for example indicators related to cancer.

After statistical analysis of these new datapoints, data interpretation can begin. MetaboINDICATOR provides references to the literature where each sum or ratio has been previously described, thus immediately expanding the perspective for application.

Let’s take the example of the ratio of acetylated to non-acetylated polyamines. Experimental work in cancer models shows that this ratio correlates strongly with SSAT enzyme expression, providing a functional handle on acetylation dynamics (Kramer et al. 2008). While the ratio of product to substrate can be used as a proxi for enzymatic activity, interpretation based on the biological context is key. What is most interesting here though is the correlation of these metabolite levels with immunotherapy response.

For someone with little to no experience in metabolomics, having this automated access to a relevant indicator and literature attached to it can save hours of literature research and put them directly on the right track for their interpretation, where the precise context of their research is going to be a strong driver of their thinking process towards translation.

Step 3 | Translate
For this step, I’ll go back to the 5P medicine concept and pick examples for predictive and precision medicine applications of metabolomics.

Predictive Medicine: Forecasting immunotherapy success
Recent clinical research demonstrates that polyamine related metabolites are predictive of patient response. Elevated plasma acetylated polyamines are associated with poor response to CAR T-cell therapy in relapsed/refractory large B cell lymphoma. A 6 marker metabolite panel (including acetylspermidine and diacetylspermidine) was validated across cohorts as a predictor of non durable response (Fahrmann et al. 2022). This was the outcome of the very first study I introduced in step 1.

Identifying a biomarker signature is one of the best-known applications of metabolomics, and with strong results in validation cohorts, this can lead to deep impact in how medicine will be practiced in the field. But I also wanted to include an example focused on new drug therapy development.

Precision Medicine: Polyamine blockade therapy
Targeting polyamine metabolism doesn’t stop at prediction; it also offers therapeutic opportunity.
A landmark study introduced polyamine blockade therapy, a dual strategy combining inhibition of polyamine synthesis (DFMO) with a polyamine transport inhibitor (Trimer PTI). Polyamine blockade therapy significantly reduced tumor growth more effectively than either inhibitor alone (Alexander et al. 2017). The anti tumor effect is T cell dependent, featuring increased cytotoxic CD8⁺ T cells and reduced immunosuppressive cell populations. By reversing the immunosuppressive effects of high polyamine levels, this approach exemplifies how metabolomics informed targets can directly shape therapeutic innovation.

This pathway spotlight illustrates how metabolomics, guided by the Screen | Leverage | Translate framework, enables deep biological insight and clinical translation. Polyamine metabolism is just one pathway among many, yet its story captures the essence of what 5P medicine strives for: using precise biochemical information to create predictive, preventive, and precise health solutions, from individual to population health.

As you explore your own datasets, I encourage you to adopt this structured approach. In particular, don’t stop at the screening step. Dig further. Leverage your data to gain a broader understanding of the metabolic changes in your system, and devise the next experiments that will yield the truly transformative solutions to transform medicine.

• How it started | The 5P medicine framework
• How metabolites tell 5P stories
• From mechanisms to applications
• Feedback from the scientific community
• 5P medicine in our podcast | The Metabolomist
• The path ahead

biocrates’ mission is to make metabolomics accessible to facilitate breakthroughs in biomedical research. In 2025, we explored this mission through a fresh lens: that of ‘5P medicine’, which builds on insights drawn from omics to rethink how medicine is practiced. We looked at how the 5P framework brings together preventive, predictive, precision, population-based and participatory medicine to nudge healthcare from reactive to truly proactive and patient-centric.

This approach is especially powerful for complex chronic diseases, where genetics alone can indicate risk but cannot track how this risk evolves with lifestyle, environment, or aging. Metabolomics helps fill that gap.

We started the year hoping to showcase the myriad applications of metabolomics in life science research. What we didn’t expect was the amazing feedback from the scientific community and the new collaborations that presented themselves along the way.

Here, we look back at the milestones of 2025, from launching our most comprehensive kit to date to joining Biognosys Group, alongside a few takeaways from our most impactful activities in 2025.

How it started | The 5P medicine framework

Our starting point was a metabolomist’s view of the 5P medicine overview (see below), which shows how preventive, predictive, precision, population-based and participatory medicine come together in one framework. Each ‘P’ connects to specific research components uniquely supported by metabolomics, including risk scores, biomarkers, stratification, environmental variance and at-home sampling, and we see why metabolomics is such a natural fit for this approach.

Building on that, we launched a five-part blog series written by Alice Limonciel , to look more closely at the role of metabolomics in 5P medicine:

“Preventive medicine” showed how metabolomics detects early metabolic shifts long before symptoms appear, enabling risk stratification, targeted lifestyle interventions and progress monitoring.

“Predictive medicine” focused on building metabolic signatures that forecast disease progression and treatment response, especially when metabolomics is combined with other omics in multiomics studies.

“Precision medicine” explored how quantitative metabolite panels reveal hidden subgroups and enable personalized prognostic/diagnostics and treatment optimization.

“Population-based medicine” scaled up to cohorts and biobanks, connecting metabolomics to exposomics while refining stratification methods through metabotyping, leveraging the power of molecular phenotypes.

“Participatory medicine” looked at at-home sampling and reference ranges for quantitative metabolomics that make medicine accessible to larger parts of the population, often at lower costs and for preventive and monitoring applications.

These articles formed the red thread of our guide to applying metabolomics in 5P medicine. You’ll find more on these themes throughout our articles, webinars, events and conference reports.

How metabolites tell 5P stories

In our “metabolite of the month” series, we look at one molecule each month and ask what it reveals about health. This year, we added a new section focused on the metabolite’s role in 5P medicine. Here are a few examples:

Methylmalonic acid (MMA)
Recognized as the most specific functional marker of vitamin B12 status, MMA holds promise for preventive and predictive medicine, with tight links to mitochondrial function, the gut microbiome and the nervous system.

Itaconic acid
Linking immune response and inflammation across obesity, cardiovascular, neurological and oncologic disease, itaconic acid is a powerful metabolite, acting as an early biomarker of inflammation and therapeutic response for preventive and predictive strategies. It’s also a promising precision target for future interventions.

Bilirubin
More than a waste product, bilirubin is a predictor of disease susceptibility, a target for preventive strategies in oxidative stress-driven disorders and a potential precision marker to guide personalized oncology and immunotherapy decisions.

p-cresol glucuronide (pCG)
Linking gut ecology to renal and systemic outcomes, pCG is measurable non-invasively in urine and blood for predictive and precision stratification, and is modifiable through diet and microbiome interventions to support preventive and participatory care.

From mechanisms to applications

While a single metabolite can play a role in many applications, some stories are better told with multiple protagonists. When using omics to describe the molecular presentation of a disease or the intricate mechanisms that support life, the focus needs to be on how groups of molecules interact. This is what we turned to in the articles below, which look at how metabolomics and lipidomics turn pathway insights into concrete clinical questions.

In “Energy metabolism in cancer – Mechanisms, plasticity and applications”, Gordian Adam, explored how metabolomics helps reveal the mechanisms driving cancer’s metabolic plasticity, from the Warburg effect and oncometabolites like lactate and 2-hydroxyglutarate to redox balance, lipid metabolism and the tumor microenvironment – view key takeaways here.

In “Peroxisome biogenesis disorders and the lipidome”, Franziska Hörburger reported from the International Conference on the Bioscience of Lipids (ICBL) 2025 that “when peroxisomes falter, lipids tell the story.” Early lipid remodeling – involving increased very long-chain fatty acids, decreased plasmalogens and decreased docosahexaenoic acid (DHA)-rich lipids – often precedes inflammation and tissue damage. These profiles are emerging as promising early markers of disease and show how targeted lipidomics can map changes and connect them to clinical phenotypes.

Feedback from the scientific community

Our 5P focus also shaped how we talked about biocrates at conferences, events and webinars.

At the American Society for Mass Spectrometry (ASMS) 2025, we launched the MxP® Quant 1000 kit , our most comprehensive kit to date, and really took the pulse of the community on the 5P concept. From the modular application of metabolomics and lipidomics to the wide-ranging applications of the results through the five pillars of 5P medicine, it all seemed to resonate really well – take a look at the highlights!

At Metabolomics 2025, we saw how strongly the community already lives the 5P medicine concept – from at-home sampling for skin microbiopsies for participatory and preventive care to metabolomics and lipidomics applications driving predictive and precision insights. For us, it confirmed that solutions like the MxP® Quant 1000 kit and insightful data interpretation are exactly what’s needed to bring omics closer to everyday practice.

5P medicine webinar

In October, Sabine Bahn and Oliver Bathe, together with Alice Limonciel, highlighted how metabolomics is transforming neurology and oncology research within the framework of 5P medicine.

Metabolomics India 2025 webinar

Every year, we put the Indian metabolomics community in the spotlight. In this year’s virtual conference, our many speakers highlighted how discoveries made with metabolomics and multiomics will be leveraged by medicine in the near future.

Whether at in-person events or through your feedback after our webinars, you’ve shown us that the 5P vision resonates widely – and that standardized and robust metabolomics solutions are an essential step in the future contribution of metabolomics into everyday medical decision-making.

5P medicine in our podcast | The Metabolomist

Every season of our podcast has a specific focus and this year, of course, our theme was 5P medicine. This allowed us to dive into vastly different fields of research, including plant metabolism, exposomics, microbial metabolism, obesity, software development, method transfer and publication.

Here is a taster of what you’ll hear in our six episodes in 2025:

Cristina Legido-Quigley discusses the relevance of lipidomics in obesity and neurology, monitoring dietary interventions with lipidomics and the importance of sex differences to precision medicine.

Tomáš Pluskal discusses the beauty and complexity of plant metabolism, sustainable ways to harness these molecules and pathways for medical applications, plus the development of one of the most widely used software tools in metabolomics.

Gary Miller discusses the synergies of exposomics and metabolomics, the next frontier in multiomics integration and how exposomics promises to bolster all 5Ps and the future of medicine.

This episode features exclusive interviews collected by Alice at the 2025 Metabolomics Society conference in Prague, Czech Republic. In these recordings, we discover the small molecules and lipids that make the hearts of ten Metabolomists beat for their research.

Anne Bendt discusses why metabolomics is a disrupting technology in the field of clinical chemistry, the most critical steps needed for its implementation in the clinics and the fascinating lipidomes of bacteria.

María Eugenia Monge discusses why building community matters in science, how to navigate the publishing world when establishing new methods in your lab and the region-specific implications of the implementation of metabolomics in the clinics.

The path ahead

If you’ve been following our activities over the last year, you’ll also know that in 2025 biocrates was acquired by Bruker and joined a new structure within this company: Biognosys Group.

Within this group, we bring expertise in metabolomics, lipidomics and mass spectrometry (MS) workflow standardization that will be paired with the expertise of other partners in proteomics and MS-based technologies. This new horizon promises to further enable the inclusion of metabolomics into broader workflows and applications that will surely support the contribution of omics to the 5P medicine framework.

To follow our next steps, make sure to register for our monthly newsletter.

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

In many ways, the conference came together like a puzzle, with workshops, posters, hallway chats and keynote insights all connecting to reveal a picture of where metabolomics stands today. Rather than a chronological recap, I’ll look at this event through the values we hold dear at biocrates: excellence, purpose, community and ownership.

A picture of excellence

Every year, the Metabolomics Society curates a program at the intersection of chemistry, biology and bioinformatics. From plenary lectures to workshops, the conference doesn’t just focus on human health, but has grown to include microbial metabolism, plant metabolism and increasingly lipidomics, another omic enabled by advances in mass spectrometry.

As I didn’t attend ASMS this year, this was my first opportunity to discuss our new kit face to face with the metabolomics community. The MxP® Quant 1000 kit has our broadest coverage to date, with a coverage of small molecules more than three times our previous flagship product. After the effort we put into developing it, it was so rewarding to see how well our offering aligned with the community’s needs. A great reminder that excellence isn’t just about innovation, it’s also about relevance.

A picture of purpose

Beneath the science, Metabolomics 2025 always feels to me like a call to action. We want to see metabolomics and lipidomics make their way from research labs to clinics. It’s only through the translation of science to practice that we can achieve a healthcare model that’s preventive, predictive, population-based, precision and participatory medicine, AKA “5P medicine”.

This year, I presented a poster on lipidomics measurements from skin microbiopsies and was thrilled to see all the posters of the “at-home sample collection” session. Such solutions are not only great examples of the participatory medicine approach, but they are also essential for the broader democratization of metabolomics.

Data interpretation is another cornerstone of translation. Over the years, I’ve made enormous efforts to make this more accessible, through The Metabolomist podcast, The STORY Principle and various other projects aimed at helping others make sense of metabolomics. I still think effective data interpretation is essential to help us explain the real-world value of metabolomics results and eventually translate them into our daily lives and medical practice.

To mark the launch, we tried something different: sharing the MxP® Quant 1000 kit’s comprehensive metabolic pathway coverage not just as an image, but also as a 1000-piece puzzle! Your reaction was amazing. So many of you competed in our contest to win one of the five puzzles we had at the booth. Thank you to everyone who made suggestions on how to take the concept further. I wouldn’t be surprised if some of your ideas appear in our next set of kit materials.

Speaking of scientific communication, I had the pleasure of hosting the “Scientific Communication” table together with Olya Vvedenskaya at the Early Career Members Network Career Night. This was a great way to start the conference week and mingle with early career scientists or scientists looking for a transition in their career. A fantastic initiative.

A picture of community

From Career Night to the lively breaks, posters sessions and workshops, Metabolomics 2025 did not disappoint when it came to community building. To be honest, I enjoyed the networking so much that I sometimes forgot to attend a session I was interested in.

It was also great to see so many people attend the vendor session where I presented our latest kit in a record five minutes! The sense of camaraderie with other speakers, each of us cramming our key messages into this short time, made it a special experience.
(If you’d like to learn more about the kit, I invite you to register for our live event celebrating the launch of the kit: “Transform medicine with metabolomics”.)

For this MetSoc, I also brought back an old favorite and asked ten attendees to tell me about their favorite metabolites. It’s a fun and simple way to break the ice and get to know people, and always entertaining to hear why you love these metabolites so much. You can listen to the MetSoc episode on The Metabolomist website and all podcast platforms.

A picture of ownership

This year’s Metabolomics conference was also marked by questions about the elephant in the room: the recent acquisition of biocrates by Bruker corporation and its impacts on biocrates’ activities going forward. In short, as manufacturers of metabolomics and lipidomics kits, it is still essential for us to build solutions that enable the use of omics regardless of the analytical platform used.

The exhibition hall where the biocrates booth was surrounded by all major mass spectrometry vendors was an especially great environment to reassure both mass spec vendors and customers that we stay committed to our multi-vendor strategy. If your instrument is already supported by our kits, or if there’s a specific instrument you’d like us to adapt the MxP® Quant 1000 kit to next, we’d love to hear from you.

The final piece in the puzzle

Metabolomics 2025 truly was a conference worth 1000 words. Despite the scale of the conference, it always feels like we are working together to push the field forward. Fittingly, this year’s event attracted around 1000 registrations. A coincidence or a sign? I’ll leave it up to you to decide.

To everyone who visited the booth, asked about the modularity of our new kit, joined a conversation over a cup of tea, or shared their favorite metabolite: thank you and I hope to see you next year in Buenos Aires!
Find out more about the MxP® Quant 1000 kit.

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

• History & Evolution
• Biosynthesis
• Spermidine, autophagy and aging
• Spermidine and cancer
• Spermidine and neurology
• Spermidine and hair
• References

History & Evolution

1924: first synthesis by Otto Rosenheim (Bachrach 2010)

Spermidine is a biogenic amine of the polyamine family, which contribute to cell division and growth. It’s also the downstream metabolite of putrescine, which is associated with bodily odors. Spermidine and its precursor, spermine, get their name from semen, after crystals of spermine were first identified in human semen in the seventeenth century (Bachrach 2010). Once considered a uremic toxin, spermidine has been put in a more positive light by recent research pointing to its role in cell homeostasis, cancer treatment, longevity, and hair growth, making it a hot topic for 5P medicine applications.

Biosynthesis and dietary uptake

Spermidine is present in large amounts in mammals. It can also be readily absorbed in the small intestine from foods such as wheat germ, soy bean, aged cheese and mushrooms (Bardócz et al. 1995). In humans, it can be synthesized by commensal bacteria in the large intestine from putrescine or arginine (Matsumoto et al. 2012; Hanfrey et al. 2011; Tofalo et al. 2019). Mammalian cells are also capable of synthesizing spermidine from putrescine, itself a product of the metabolism of arginine, linking polyamines to the urea cycle (Madeo et al. 2018). In humans, circulating levels of spermidine are often in the low micromolar range, although they show a strong inter-individual variability. This is most likely due to the effect of diet on overall spermidine concentration (Soda et al. 2009).

Spermidine, autophagy and aging

In the last decade, spermidine has attracted interest as a promoter of longevity. In 2009, Eisenberg et al. linked spermidine to increased lifespan, reduced oxidative stress, and reduced necrosis across species. The effect of spermidine on acetylation of proteins and chromatin was identified as a key mechanism for the modulation of autophagy, increasing the lifespan of yeast, flies, worms, and human cells (Eisenberg et al. 2009). Autophagy, the mechanism by which cells remove malfunctioning proteins and organelles, is an essential player in cellular homeostasis. This makes it a major target of drug development for conditions as varied as cancer, Alzheimer’s disease, and cardiovascular diseases (Onorati et al. 2018; Ren et al. 2018; Uddin et al. 2018). As an endogenous activator of autophagy, spermidine has attracted a lot of attention for dietary intervention and drug development.

Spermidine and cancer

Polyamines were originally relevant to cancer research because of their role in cell proliferation and growth. Disturbance of polyamine metabolism was observed in several types of cancer, including skin, breast, lung, prostate, and colon (Madeo et al. 2018; Nowotarski et al. 2013). However, more recent cancer research studies have focused on spermidine as a caloric restriction mimetic (CRM) with a positive impact. CRMs are molecules that limit cancer cells’ access to nutrients, thus making them more vulnerable to anti-cancer treatment. As an autophagy activator, spermidine has potential for both cancer prevention and treatment, especially to limit tumor growth (Fan et al. 2020; Kocaturk et al. 2019). In 2018, a prospective population-based study linked high levels of spermidine intake to lower mortality, including mortality from cancer (Kiechl et al. 2018). Researchers are therefore exploring both spermidine supplementation and more elaborate treatments such as the use of spermidine analogues targeting the DNA of tumor cells (Wang et al. 2018).

Spermidine and neurology

Circulating spermidine levels decrease with age (Pekar et al. 2020). This, and the fact that autophagy is of interest in the treatment of neurodegenerative diseases (De Risi et al. 2020), makes spermidine an interesting candidate for further investigation. In patients with Parkinson’s disease (PD), the spermine to spermidine ratio in plasma was strongly decreased, suggesting an inhibition of the enzyme spermine synthase (Saiki et al. 2019). Interestingly, PD patients were found to have elevated levels of acetylated forms of spermidine and putrescine, and a general hyperacetylation consistent with autophagy activation. In a randomized controlled Phase IIa trial, supplementation with spermidine showed a positive impact on memory performance after just three months of administration in older adults at risk of dementia (Wirth et al. 2018). In the senescence accelerated mouse model SAMP8, spermidine was shown to slow neurodegeneration, restoring impaired mitochondrial function and reducing inflammation (Xu et al. 2020). Spermine and rapamycin (a classical activator of autophagy via the mTOR pathway), induced similar effects, although spermine tended to be less effective.

Spermidine and hair

A more exotic field for the application of spermidine research is hair growth and hair loss. In vitro studies on human scalp and hair follicle epithelial stem cells revealed the effect of spermidine on hair growth, but also on the regulation of epithelial stem cells (Ramot et al. 2011). A 90-day study showed that spermidine supplementation promoted hair growth and resistance in human subjects (Rinaldi et al. 2017). Immunohistochemical staining in rat hair follicles revealed the distribution patterns of putrescine, spermidine and spermine in the regions most linked to hair growth (Yamamoto et al. 2018). However, only the polyamines were present in high amounts in the epidermis and fibroblasts.

References

Bachrach et al. : The early history of polyamine research (2010) Plant Physiology and Biochemistry | doi: 10.1016/j.plaphy.2010.02.003

Bardócz; et al. : The importance of dietary polyamines in cell regeneration and growth (1995) British Journal of Nutrition | doi: 10.1079/BJN19950087

Eisenberg et al. : Induction of autophagy by spermidine promotes longevity (2009) Nature cell biology | doi: 10.1038/ncb1975

Fan et al.: Spermidine as a target for cancer therapy (2020) Pharmacological Research | doi: 10.1016/j.phrs.2020.104943

Hanfrey et al.: Alternative Spermidine Biosynthetic Route Is Critical for Growth of Campylobacter jejuni and Is the Dominant Polyamine Pathway in Human Gut Microbiota * (2011) Journal of Biological Chemistry | doi: 10.1074/jbc.M111.307835

Kiechl et al.: Higher spermidine intake is linked to lower mortality: a prospective population-based study (2018) The American journal of clinical nutrition | doi: 10.1093/ajcn/nqy102

Kocaturk et al.: Autophagy as a molecular target for cancer treatment (2019) European journal of pharmaceutical sciences : European Federation for Pharmaceutical Sciences | doi: 10.1016/j.ejps.2019.04.011

Madeo et al.: Spermidine in health and disease (2018) Science | doi: 10.1126/science.aan2788

De Risi et al.: Mechanisms by which autophagy regulates memory capacity in ageing (2020) Aging Cell | doi: 10.1111/acel.13189

Matsumoto et al.: Impact of Intestinal Microbiota on Intestinal Luminal Metabolome (2012) Scientific Reports |  doi: 10.1038/srep00233

Nowotarski et al.: Polyamines and cancer: implications for chemotherapy and chemoprevention (2013) Expert Reviews in Molecular Medicine | doi: 10.1017/erm.2013.3

Onorati et al.: Targeting autophagy in cancer (2018) Cancer | doi: 10.1002/cncr.31335

Pekar et al.: Spermidine in dementia (2020) Wiener klinische Wochenschrift | doi: 10.1007/s00508-019-01588-7

Ramot et al.: Spermidine promotes human hair growth and is a novel modulator of human epithelial stem cell functions (2011) PloS one | doi: 10.1371/journal.pone.0022564

Ren et al.: Metabolic Stress, Autophagy, and Cardiovascular Aging: from Pathophysiology to Therapeutics (2018) Trends in endocrinology and metabolism: TEM | doi: 10.1016/j.tem.2018.08.001

Rinaldi et al.: A spermidine-based nutritional supplement prolongs the anagen phase of hair follicles in humans: a randomized, placebo-controlled, double-blind study (2017) Dermatology practical & conceptual | doi: 10.5826/dpc.0704a05

Saiki et al. : A metabolic profile of polyamines in parkinson disease: A promising biomarker (2019) Annals of neurology | doi: 10.1002/ana.25516 .

Soda et al.: Long-Term Oral Polyamine Intake Increases Blood Polyamine Concentrations (2009) Nutritional Science and Vitaminology | doi: 10.3177/jnsv.55.361

Tofalo et al.: Polyamines and Gut Microbiota (2019) Frontiers in Nutrition | doi: 10.3389/fnut.2019.00016

Uddin et al.: Autophagy and Alzheimer’s Disease: From Molecular Mechanisms to Therapeutic Implications (2018) Frontiers | doi: 10.3389/fnagi.2018.00004

Wang et al.: Isothermal Self-Assembly of Spermidine-DNA Nanostructure Complex as a Functional Platform for Cancer Therapy (2018) ACS | doi: 10.1021/acsami.8b03464

Wirth et al.: The effect of spermidine on memory performance in older adults at risk for dementia: A randomized controlled trial (2018) Cortex | doi: 10.1016/j.cortex.2018.09.014

Xu et al.: Spermidine and spermine delay brain aging by inducing autophagy in SAMP8 mice (2020) Aging | doi: 10.18632/aging.103035

Yamamoto et al.: Expression and distribution patterns of spermine, spermidine, and putrescine in rat hair follicle (2018) Histochemistry and Cell Biology | doi: 10.1007/s00418-017-1621-1

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!

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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

• History & Evolution
• Biosynthesis vs. dietary uptake
• Propionic acid and the microbiome
• Propionic acid and signaling
• Propionic acid and neurology
• Propionic acid and oncology
• Propionic acid and 5P medicine

History & Evolution

1844: discovery (Gottlieb at al. 1844) | 1847: first synthesis (Dumas et al. 1847) | 1980s: elucidation of metabolic pathways

Propionic acid was first identified in 1844 by chemist Johann Gottlieb, during investigations into the effect of potassium hydroxide on sugar (Gottlieb et al. 1844). A few years later, French chemist Jean-Baptiste Dumas realized that other chemists had unknowingly produced the same compound by different means. He named the metabolite from the Greek words proto (“first”) and pion (“fat”), noticing that it was the smallest carboxylic fatty acid (Dumas et all. 1847). In 1878, Albert Fitz demonstrated bacterial synthesis of propionic acid, establishing the Fitz equation to describe its production by Propionibacteria during lactic acid fermentation (Bezirci et al. 2023). Subsequent research revealed antimicrobial and antifungal properties, leading to widespread use of propionic acid as a food preservative (Liu et al. 2012).

In recent decades, research has focused on propionic acid’s properties as a short chain fatty acid (SCFA) and its metabolic pathways in the human gut, highlighting the importance of microbial fermentation in energy homeostasis and overall health (den Besten et al. 2013). Propionic acid’s anti-inflammatory and anti-proliferative properties make it relevant in cancer research, and it plays a key role in lipid metabolism, neurological function, and as a biomarker for metabolic diseases such as obesity and diabetes (Zhang et al. 2023).

Propionic acid occurs naturally in humans, plants, microorganisms and animals. It has a wide range of industrial applications, including cheesemaking, preservatives, animal feed, pharmaceuticals, cosmetics and paints (Liu et al. 2012). It has a notoriously rancid smell, contributing to body odor and certain pungent cheeses (PubChem, 2025).

Biosynthesis vs. dietary uptake

In humans, propionic acid is derived from endogenous and environmental sources (Frye et al. 2016). Endogenous synthesis occurs during the catabolism of odd-chain fatty acids and branched-chain amino acids such as isoleucine and valine (Snyder et al. 2015). In these pathways, propionyl-CoA is formed and converted into methylmalonyl-CoA, which then is converted to succinyl-CoA and enters the TCA cycle.

Most propionic acid is synthesized by gut microbiota through the fermentation of dietary fibers (Louis et al. 2017). Microbial biosynthesis involves several distinct pathways in the gut, each characterized by different substrates and enzymatic reactions (Chen et al. 2025). These include:

• the succinate pathway, which converts succinate to propionate
• the acrylate pathway, which uses lactate and sugars as intermediates
• the propanediol pathway, which metabolizes deoxy sugars like rhamnose and fucose
• the dicarboxylic acid (Wood-Werkman) pathway, which involves the fermentation of pyruvate to propionate through intermediates such as oxaloacetate and methylmalonyl-CoA into propionic acid (Ranaei et al. 2020).

These pathways vary greatly depending on diet, microbiome diversity and host health (Chen et al. 2025).

Humans can also directly absorb propionic acid through the consumption of fermented foods. Although dietary propionic acid contributes modestly compared to microbial production, it still influences circulating plasma propionic acid levels (Chen et al. 2025). Diets rich in fermentable dietary fibers have been found to increase production of propionic acid by gut microbiota, while low-fiber diets are linked to decreased endogenous propionic acid synthesis and reduced plasma levels (Vinelli et al. 2022).

Once synthesized, PA is absorbed from the colon via the portal vein, with concentrations typically ranging between 100 and 300 µM, with around 6 μM in peripheral blood (Snyder et al. 2015). Most propionic acid is metabolized in the liver, leaving acetate as the most abundant SCFA in the periphery. Propionic acid is converted in the liver to propionyl-CoA, which can then be converted to pyruvate and used in energy metabolism and gluconeogenesis (Chen et al. 2025).

Plasma levels can also be affected by conditions like propionic acidemia, a rare inherited disorder, in which propionyl-CoA carboxylase deficiencies cause propionic acid to accumulate (Chen et al. 2025).

Elevated acetic acid-to-propionic acid ratios have been associated with obesity and insulin resistance (Müller et al. 2019). This is because acetic acid promotes lipogenesis and fat accumulation, while propionic acid acts as a counter-regulator, inhibiting fat synthesis and promoting glucose homeostasis. Thus, an imbalance where acetic acid dominates over propionic acid may result in increased fat storage and higher risk of developing metabolic syndrome.

Propionic acid and the microbiome

Different bacterial species contribute to the above pathways. For example, the succinate pathway is commonly found in Bacteroidetes and Firmicutes (Louis et al. 2017). The acrylate pathway operates in in Lachnospiraceae, and the propanediol pathway is observed in gut commensal bacteria including Roseburia inulinivorans and Blautia.

Alterations in microbiota can reduce propionic acid synthesis affecting gut integrity, immune function and metabolic processes (Zhang et al. 2023). For example, studies show that SCFA-producing bacteria are significantly reduced in patients with inflammatory bowel disease (IBD), and concentrations of propionate are reduced in the intestinal microbiome of these patients (Zhang et al. 2023), (Sun et al. 2017).

Propionic acid is also linked to the skin microbiome: species of Propionibacteria such as Cutibacterium acnes are found in the sebaceous glands, and are a recognized cause of acne (Bojar et al. 2004).

An animal model suggests that Bacteroides produces propionic acid in the gut, directly inhibiting the proliferation of the intestinal bacterial pathogen Salmonella (Jacobson et al. 2018).

Propionic acid and signaling

Like other SCFAs, propionic acid is an important signaling molecule involved in metabolic, immunological, neurological and cardiovascular processes (Zhang et al. 2023). This occurs primarily through activation of specific G protein-coupled receptors (GPCRs), particularly GPR41, affecting the pathways that regulate glucose homeostasis, immune responses and lipid metabolism (Zhang et al. 2023).

Propionic acid also modulates gene expression epigenetically by inhibiting histone deacetylases, affecting apoptosis, inflammation and immune function. Alongside acetic acid and butyric acid, propionic acid is an energy source for intestinal epithelial cells, promoting proliferation, differentiation and barrier integrity.

Dysregulation of propionic acid signaling pathways has been linked to metabolic disorders, neurodegenerative diseases, IBD and cardiovascular diseases, making propionic acid and related SCFAs promising therapeutic targets (Zhang et al. 2023).

Propionic acid and neurology

Propionic acid is increasingly recognized for its role in neurological health, particularly through the gut-brain axis (Killingsworth et al. 2021). Elevated propionate levels have been linked to behavioral and neurological changes observed in autism spectrum disorders (ASD), along with alterations in mitochondrial function, neuroinflammation, neurotransmitter imbalances and oxidative stress (MacFabe et al. 2012). Animal studies demonstrate that increased exposure to propionic acid can induce autism-like behaviors, underscoring its potential influence on neurodevelopment and cognition (Schulz et al.).

Abnormal regulation of propionic acid is also implicated in Alzheimer’s disease, where its role in inflammatory pathways and brain metabolism positions it both as a potential biomarker and therapeutic target (Killingsworth et al. 2021).

Propionic acid holds promise in neurodegenerative diseases such as Parkinson’s disease (PD) and multiple sclerosis (MS). In an animal model of PD, both depletion of dietary vitamin B12 (to induce propionic acid breakdown) and propionic acid supplementation were found to suppress neurodegeneration (Wang et al. 2024).

Clinical findings support this relationship, showing that decreased serum propionic acid levels correlated with more severe motor dysfunction and cognitive impairment in patients with PD (Wu et al. 2022).

Similarly, emerging metabolomic and clinical studies in MS suggest that propionic acid may exert beneficial immunoregulatory and neuroprotective effects (Lorefice et al. 2024) (Duscha et al. 2020). Propionate supplementation has been shown to boost regulatory T cells (Tregs), reduce pro-inflammatory Th1 and Th17 responses, and decrease brain atrophy, ultimately improving clinical outcomes in MS patients (Duscha et al. 2020). These findings underscore propionic acid’s potential as a complementary therapeutic strategy, although further clinical trials are needed to establish its efficacy and mechanisms.

Propionic acid and oncology

Recent studies point to the importance of propionic acid in oncology. Research in colorectal cancer indicates that propionic acid can inhibit tumor cell proliferation, potentially by inducing apoptosis and reducing inflammation (Young et al. 2022). In cervical cancer, propionic acid has been found to promote cell death by inducing mitochondrial dysfunction, apoptosis, autophagy and the accumulation of reactive oxygen species (Pham et al. 2021).

While more clinical trials are needed, these observations suggest that propionic acid holds potential as a biomarker of disease and therapeutic target. Encouragingly, propionic acid may be a suitable candidate for clinical trials, given that it is already approved for consumption as a food preservative.

Propionic acid and 5P medicine

Propionic acid is also worth investigating in the context of 5P medicine (preventive, predictive, precision, population-based, and participatory medicine). Propionic acid is of interest for its association with metabolic disorders such as obesity, insulin resistance and type 2 diabetes, suggesting a role in disease prevention and prognosis (Chambers et al. 2015). It has been shown to reduce obesity through activation of G-coupled protein receptors, leading to satiety, lower energy intake and weight loss (Chen et al. 2025). Given the association between obesity and other metabolic disorders, this suggests Propionic acid may be a good candidate for population-wide health interventions.

References

Bezirci, E. et al: Propionic acid production via two-step sequential repeated batch fermentations on whey and flour (2023) Biochemical Engineering Journal | DOI: https://doi.org/10.1016/j.bej.2023.108816

Bojar, R. et al: Acne and propionibacterium acnes Author links open overlay panel (2004) Clinics in Dermatology | DOI: https://doi.org/10.1016/j.clindermatol.2004.03.005

Chambers, E. et al: Effects of targeted delivery of propionate to the human colon on appetite regulation, body weight maintenance and adiposity in overweight adults (2015) Gut | DOI: https://doi.org/10.1136/gutjnl-2014-307913

Chen, X. et al: Elevated propionate and its association with neurological dysfunctions in propionic acidemia (2025) Front Mol Neurosci. | DOI: https://doi.org/10.3389/fnmol.2025.1499376

den Besten, G. et al: The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism (2013) J Lipid Res. | DOI: https://doi.org/10.1194/jlr.R036012

Dumas, M. et al: Sur l’identité des acides métacétonique et butyro-acétique [On the identity of metacetonic acid and butyro-acetic acid] (1847) Comptes rendus

Duscha, A. et al: Propionic acid shapes the multiple sclerosis disease course by an immunomodulatory mechanism (2020) Cell | DOI: https://doi.org/10.1016/j.cell.2020.02.035

Frye, R. et al: Modulation of mitochondrial function by the microbiome metabolite propionic acid in autism and control cell lines (2016) Translational Psychiatry | DOI: https://doi.org/10.1038/tp.2016.189

Gottlieb, J. et al: On the effect of molten potassium hydroxide on raw sugar, rubber, starch powder, and mannitol (1844) Annalen der Chemie und Pharmacie

Jacobson, A. et al: A gut commensal-produced metabolite mediates colonization resistance to Salmonella infection (2018) Cell Host Microbe | DOI: https://doi.org/10.1016/j.chom.2018.07.002

Killingsworth, J. et al: Propionate and Alzheimer’s Disease (2021) Front. Aging Neurosci. | DOI: https://doi.org/10.3389/fnagi.2020.580001

Liu, L. et al: Microbial production of propionic acid from propionibacteria: current state, challenges and perspectives (2012) Crit Rev Biotechnol | DOI: https://doi.org/10.3109/07388551.2011.651428

Lorefice, L. et al: Propionic acid impact on multiple sclerosis: evidence and challenges (2024) Nutrients | DOI: https://doi.org/10.3390/nu16223887

Louis, P. et al: Formation of propionate and butyrate by the human colonic microbiota (2017) Environmental Microbiology | DOI: https://doi.org/10.1111/1462-2920.13589

MacFabe, D. et al: Short-chain fatty acid fermentation products of the gut microbiome: implications in autism spectrum disorders (2012) Microb Ecol Health Dis. | DOI: https://doi.org/10.3402/mehd.v23i0.19260

Müller, D. et al: Circulating but not faecal short-chain fatty acids are related to insulin sensitivity, lipolysis and GLP-1 concentrations in humans (2019) Scientific reports | DOI: https://doi.org/10.1038/s41598-019-48775-0

Pham, C. et al: Anticancer effects of propionic acid inducing cell death in cervical cancer cells (2021) Molecules | DOI: https://doi.org/10.3390/molecules26164951

Propionic acid-compound (2025) Pubchem | https://pubchem.ncbi.nlm.nih.gov/compound/Propionic-Acid

Ranaei, V. et al: Propionic acid: Method of production, current state and perspectives (2020) Food Technol Biotechnol | DOI: https://doi.org/10.17113/ftb.58.02.20.6356

Schulz, S. et al: Propionic acid animal model of autism (n.d.) Comprehensive Guide to Autism | DOI: https://doi.org/10.1007/978-1-4614-4788-7_106

Snyder, N. et al: Metabolism of propionic acid to a novel acyl-coenzyme A thioester by mammalian cell lines and platelets (2015) J Lipid Res. | DOI: https://doi.org/10.1194/jlr.M055384

Sun, M. et al: Microbiota metabolite short chain fatty acids, GPCR, and inflammatory bowel diseases (2017) Journal of Gastroenterology | DOI: https://doi.org/10.1007/s00535-016-1242-9

Vinelli, V. et al: Effects of dietary fibers on short-chain fatty acids and gut microbiota composition in healthy adults: A systematic review (2022) Nutrients | DOI: https://doi.org/10.3390/nu14132559

Wang, C. et al: Metabolic rescue of α-synuclein-induced neurodegeneration through propionate supplementation and intestine-neuron signaling in C. elegans (2024) Cell Reports | DOI: https://doi.org/10.1016/j.celrep.2024.113865

Wu, G. et al: Serum short-chain fatty acids and its correlation with motor and non-motor symptoms in Parkinson’s disease patients (2022) BMC Neurology | DOI: https://doi.org/10.1186/s12883-021-02544-7

Young, T. et al: Human gut-microbiome-derived propionate coordinates proteasomal degradation via HECTD2 upregulation to target EHMT2 in colorectal cancer (2022) The ISME Journal | DOI: https://doi.org/10.1186/s12964-023-01219-9

Zhang, D. et al: Short-chain fatty acids in diseases (2023) Cell Communication and Signaling | DOI: https://doi.org/10.1186/s12964-023-01219-9

]]> https://biocrates.com/predictive-medicine-transform-with-metabolomics/ Tue, 11 Mar 2025 09:58:13 +0000 https://biocrates.com/?p=276585 This is the second in a series of five blogs where I’ll discuss how metabolomics is set to transform medicine as we know it. The applications of omics in biomedical research are vast, and to help organize the different ways metabolomics can be used, I’ll discuss this in the context of 5P medicine.

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.

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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

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