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

An introduction to 5P medicine

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

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

Omics and the future of research and health

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

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

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

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

Participatory medicine with metabolomics

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

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

Sample collection to make healthcare more accessible

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

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

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

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

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

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

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

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

The need for reference ranges

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

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

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

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

Outlook

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

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

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

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

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

Learn more about 5P medicine in our other articles:

References

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

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

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

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

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

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

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

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

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

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

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

An introduction to 5P medicine

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

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

Omics and the future of research and health

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

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

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

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

Population-based medicine with metabolomics

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

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

• Metabolic risk scoring
• Metabotyping
• Multiomics integration

Metabolic risk scores

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

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

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

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

Metabotyping

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

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

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

Multiomics integration

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

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

Read more about how to perform mGWAS here.

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

Outlook

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

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

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

An introduction to 5P medicine

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

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

Omics and the future of research and health

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

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

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

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

Precision medicine with metabolomics

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

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

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

Personalized prognostic and diagnostics

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

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

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

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

Treatment optimization

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

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

Nutritional guidance

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

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

Outlook

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

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

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

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

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

References

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

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

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

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

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

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

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

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

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

An introduction to 5P medicine

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

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

Omics and the future of research and health

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

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

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

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

Predictive medicine with metabolomics

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

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

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

Prognosis

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

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

Pharmacometabolomics

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

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

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

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

Outlook

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

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

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

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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

An introduction to 5P medicine

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

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

Omics and the future of research and health

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

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

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

Preventive medicine with metabolomics

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

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

Dietary intervention

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

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

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

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

Probiotics

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

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

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

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

Postbiotics

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

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

Metabolic risk scores

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

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

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

Outlook

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

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

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References

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

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

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

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

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

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

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

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

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

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

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

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

Get a fuller picture of microbiome, diet, and energy metabolism

Short- and medium-chain fatty acids in health and disease

Short-chain fatty acids (SCFAs) and medium-chain fatty acids (MCFAs) are well-established as important diet-based energy sources.

In recent years, their role as signaling molecules involved in the regulation of carbohydrate and lipid metabolism has been revealed in a growing number of studies (reviewed by Schönfeld and Wojtczak 2016). Genetic defects in the metabolism of both SCFAs and MCFAs can result in inborn errors of metabolism.

Persistent aberrations in SCFA and MCFA concentrations, such as those caused by a western-style diet, may lead to inflammation and insulin resistance, eventually resulting in disorders including diabetes, neurodegenerative diseases, and cancer.

Could the combined analysis of SCFAs and MCFAs offer fresh insights into diet-microbiome-host interactions, energy homeostasis, and health status?

Short-chain fatty acids (SCFAs)

In mammals, SCFAs are primarily generated by the gut microbiome through fermentation of dietary fibers. Dietary proteins and peptides can also be a source of SCFAs.

The branched-chain amino acids valine, leucine, and isoleucine can be metabolized by intestinal bacteria to branched-chain fatty acids (BCFAs) like isobutyric acid and isovaleric acid.

These BCFAs are crucial mediators of the gut-brain axis, modulating the release of the neurotransmitter serotonin by specialized cells in the gut epithelium.

The most abundant SCFAs – acetic acid, propionic acid, and butyric acid – perform tissue-specific regulatory functions and are central mediators of the gut-immune axis.

In general, SCFAs boost immunity and promote metabolic health via the following mechanisms:

• Binding to G-protein coupled receptors (GPCRs)
• Inhibiting of histone deacetylases (HDACs)
• Integrating with the central energy metabolism (tricarboxylic acid cycle and β-oxidation)

Several studies have shown that supplementation with prebiotic SCFAs or precursors or probiotic SCFA-producing bacteria, promote health effects including weight reduction, reduction of fasting glucose levels, and increased insulin release (reviewed by Koh et al. 2016).

Medium-chain fatty acids (MCFAs)

MCFAs, such as caprylic acid and lauric acid, derive mainly from dietary medium-chain triglycerides, as found in dairy milk or palm kernel oil.

Circulating free MCFAs are not only energy fuels and precursors of ketone bodies, but they also act as tissue-specific metabolic signaling molecules involved in the regulation of inflammatory processes and insulin sensitivity.

This occurs through the following mechanisms:

• Interaction with cell membrane G-protein coupled receptors (GPCRs)
• Binding to the nuclear receptor peroxisome proliferator-activated receptor gamma (PPARY)
• Interaction with the cytosolic second messenger adenosine monophosphate (AMP)

MCFAs may have beneficial effects on glucose and lipid metabolism, and have been shown to have ameliorating effects on metabolic and neurological disorders in clinical trials (reviewed by Huang et al. 2021).

Consider your pre-analytical factors

In metabolomic studies, pre-analytical factors play a fundamental role. The concentration of a metabolite measured in a sample may be affected by when and how a sample is collected, processed, and stored.

When using fecal samples, the site from where an aliquot is drawn already makes a difference. Fecal samples are highly inhomogeneous, so pooling aliquots from different sites instead of a single spot can minimize the variance in SCFA and MCFA concentrations.

A multi-spot sampling approach is highly recommended to compensate for intra-sample variations and obtain reliable and comparable data.

For serum and plasma samples, storage time and temperature can affect the stability of certain SCFAs and MCFAs. Interestingly, SCFA concentrations often increase with time and temperature, probably due to continuous degradation of other metabolites.

This means that blood-based samples should be collected within a relatively short timeframe and stored as soon as possible at -80°C to ensure stability before analysis.

Overall, it is essential to collect and treat the samples used in a study for the analysis of SCFAs and MCFAs as consistently as possible. Find more detailed information on how to handle fecal, serum, and plasma samples for the analysis of SCFAs and MCFAs in the Application note 35045-SCFA+ assay (v1-2021).

If you are interested in measuring SCFAs or MCFAs, have a look at the SCFA+ assay. This will allow you to get a fuller picture of diet-microbiome-host interactions, energy homeostasis, and health status.

References

Huang L, Gao L, Chen C.: Role of Medium-Chain Fatty Acids in Healthy Metabolism: A Clinical Perspective (2021) Trends Endocrinol Metab | https://doi.org/10.1016/j.tem.2021.03.002

Koh A, Vadder F de, Kovatcheva-Datchary P, Bäckhed F.: From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites (2016) Cell | https://doi.org/10.1016/j.cell.2016.05.041

Schönfeld P, Wojtczak L. Short- and medium-chain fatty acids in energy metabolism: the cellular perspective (2016) J. Lipid Res. | https://doi.org/10.1194/jlr.R067629