• 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.

What was discussed?

The symposium covered a lot of ground in exploring how omics research collaborations are shaping a better understanding of health and disease, including:

• how omics technologies are being used to stratify patients with major chronic diseases and better understand rare diseases
• how the largest biobanking projects are contributing to population health
• how experimental research can be integrated with epidemiology to discover even more about individual variability
• the pitfalls and challenges of clinical cohort study design, including data collection, data protection and data integration
• the advantages of using metabolomics and other omics to collect extensive and diverse data sets (in compliance with FAIR data standards)
• the latest developments in data science tools and AI (and how to overcome data protection hurdles)
• new methods to integrate scientific findings to inform drug repurposing efforts and new lines of experimental research.

Reaffirming the value of cohort studies

Chronic disease presents an immense burden internationally, inhibiting individual and community well-being and putting strain on national finances. In the US, around 90 % of the healthcare budget is allocated to chronic diseases (CDC 2022) .The health economic impact for diabetes alone is expected to exceed €20 billion in Germany (Köster et al. 2012) and $300 billion in the US (American Diabetes Association 2018). These effects are likely to become amplified as populations age.

Cohort studies may be resource-intensive, but in this context, they are a worthy investment. Well-run cohort studies can help challenge the rising impact and cost of such diseases by increasing our understanding of these conditions so we can manage them most effectively.

To that end, Prof. Annette Peters’ fantastic keynote lecture demonstrated how cohort studies can help understand the five major groups of non-communicable diseases defined by WHO, and the associated risk factors.

Collaboration: from ideas to impact

Research in complex diseases and large cohort studies requires a wide range of expertise. Through this symposium, we sought to bring together a community of experts, and highlight the essential ingredients for successful epidemiology research, including biobanking standards, analytical expertise, and innovative bioinformatics tools.

Even the largest regional or national cohort cannot cover all factors that contribute to the variability in disease risk and outcomes, so sharing data on common risks and confounders, such as lifestyle, genetic background and socioeconomic status, is vital.
It was great to hear how biobanks and cohort studies in Asia, Europe and North America are emphasizing data interoperability.

We heard about several specific examples of how reproduction of results is achieved by design (such as the EPIC study, the SHIP studies with sister cohorts in Brazil and Poland, or the BRAINcommons platform). Other talks demonstrated successful reproduction of specific results through individual collaboration between cohorts. No one shied away from the challenges, and it was especially useful to see how others overcome the obstacles we may face in our own research.

Thank you!

A huge thank you to everyone who helped make the symposium a reality, especially our 40+ speakers who generously committed their time and expertise. We also owe gratitude to the local hosts, Prof. Masayuki Yamamoto (for Sendai), Prof. Annette Peters and Prof. Markus Gerhard (for Munich), and Prof. Jessica Lasky-Su (for Boston).
We hope you enjoyed the symposium as much as we did. Don’t hesitate to let us know your biggest takeaway and reach out to us if you have any questions about applying omics technologies to your study.

References

American Diabetes Association: Economic Costs of Diabetes in the U.S. in 2017 (2018) National Library of Medicine | http://doi.org/10.2337/dci18-0007

Centers for disease Control and Prevention (CDC): Health and Economic Costs of Chronic diseases (Entered October 18, 2022) | https://www.cdc.gov/chronicdisease/about/costs/index.htm

Köster I. et al.: Follow up of the CoDiM-Study: Cost of diabetes mellitus 2000–2009 (2012) Dtsch Med Wochenschr | http://doi.org/10.1055/s-0032-1304891

Changes in cholesterol metabolism have been associated with an elevated risk of developing Alzheimer’s disease (AD), with several studies linking hypercholesterinemia and certain lipoprotein isoforms (ApoE4) to the disease. Pan et al. (2017) and Mahmoudian Dehkordi et al. (2019) discussed the role of bile acids in AD and cognitive decline. However, the underlying biochemical mechanism has not been fully understood.

Proposed mechanisms include the effect of “classic” cardiometabolic disease risk factors, altered lipidation of lipoproteins, and effects on lysosomal plaque clearance.

In a new study, Varma et al. add to our knowledge of how brain cholesterol metabolism affects the pathogenesis of AD. The group quantitatively analyzed free oxysterols, key intermediates of cholesterol metabolism, using post-mortem brain samples from two cohort studies (the Baltimore Longitudinal Study of Aging (BLSA) and the Religious Orders Study (ROS)).

While free cholesterol levels were unchanged in AD patients in both cohorts, changes were spotted in selected markers for cholesterol anabolism and enzymatic and non-enzymatic cholesterol catabolism. For example, reduced lanosterol levels in the middle frontal gyrus were associated with a higher plaque burden in the presence of neurofibrillary tangles.

Lower levels of enzymatically-produced 24S-hydroxycholesterol and higher levels of 7α hydroxycholesterol were associated with selected markers of AD pathology. Higher amounts of non-enzymatically produced metabolites 5α,6α-epoxycholesterol, 5α,6β-dihydroxycholestanol, 5β,6β-epoxycholesterol, 7-ketocholesterol, and 7β-hydroxycholesterol were found in the inferior temporal gyrus in both cohorts. However, two of these metabolites (5α,6β-dihydroxycholestanol concentration and 7β-hydroxycholesterol) were reduced in AD patients in the ROS cohort only. Gene expression in cholesterol anabolism and catabolism were also altered in AD, though these changes were not observed in patients with Parkinson’s disease.

Besides decreased cholesterol biosynthesis, the findings suggest that AD pathogenesis is fueled by an increase in the conversion of cholesterol to bile acids, as well as an increase in the production of potentially toxic oxysterol metabolites. The authors discuss that the observed changes in cholesterol metabolism may have deleterious effects in two key ways: lower levels of cholesterol precursors may result in a loss of neuroprotective effects on mitochondrial energy metabolism, while lower levels of non-enzymatically produced metabolites may contribute to oxidative stress and chronic inflammation.

Finally, the study suggests that cholesterol metabolism might be a promising therapeutic target in Alzheimer’s disease.

Interested in the role of metabolism in neurodegenerative diseases? Explore our related articles: “Microbiome and cognition in Alzheimer´s disease” or “Is Alzheimer’s a metabolic disease?”

Varma, V.R., Büsra Lüleci, H., Oommen, A.M. et al.: Abnormal brain cholesterol homeostasis in Alzheimer’s disease—a targeted metabolomic and transcriptomic study.(2021) npj aging and mechanism of disease | https://doi.org/10.1038/s41514-021-00064-9

Chronic kidney disease (CKD) describes the loss of kidney function and affects 8-16% of the adults worldwide. It causes many age-related disabilities and even death. While CKD often leads to renal replacement, 80-90% of cases are asymptomatic. New biomarkers associated with reduced kidney function could help to diagnose CKD earlier, and provide insights into disease pathogenesis.

In a recent study, Yuko Yamaguchi from the Johns Hopkins University School of Medicine and colleagues looked for correlations between plasma metabolites and estimated glomerular filtration rate (eGFR) as well as with diagnosis of CKD. They used data from participants in the Baltimore Longitudinal Study of Aging (BLSA). The BLSA is a large, well-defined dataset, based on highly standardized procedures for data collection and laboratory analysis.

Firstly, the research team found that smokers have a higher risk of developing CKD. Next, the study revealed a high diversity of metabolites associated with CKD and eGFR. These findings suggest various metabolic pathways are involved in the disease pathophysiology, such as the urea cycle, the arginine-nitric oxide pathway, the polyamine pathway, and short chain acylcarnitine metabolism. Twenty-two metabolites were related to higher odd ratios of CKD. Higher levels of symmetric dimethylarginine (SDMA) and asymmetric dimethylarginine (ADMA) levels in individuals with CKD may inhibit the arginine-nitric oxide pathway, leading to reduced levels of the urea cycle metabolite citrulline.

Fifty-one metabolites and the putrescine/ornithine ratio were negatively correlated with eGFR. The reduction of the ornithine/citrulline and proline/citrulline levels in individuals with CKD and lower eGFR suggests impaired arginase activity and polyamine synthesis. Similarly, levels of glycine compared to serin were elevated. A lower eGFR is suggested to cause alterations of short-chain acylcarnitine metabolism in patients with CKD. Finally, the levels of some metabolites, such as hippuric acid or 1-methylhistidine, suggest differences in dietary intake and gut microbiome between participants with CKD and participants without CKD.

Using robust statistical methods, and accounting for possible confounders, the study provides insights into the pathogenesis of CKD. These findings may be the basis for further analysis to identify possible biomarkers for CKD development and progression.

Applying metabolomics in large cohort studies has become the standard for biomarker discovery – for further examples, please observe our epidemiology literature section.

Yamaguchi Y, Zampino M, Moaddel R, Chen TK, Tian Q, Ferrucci L et al.: Plasma metabolites associated with chronic kidney disease and renal function in adults from the Baltimore Longitudinal Study of Aging. (2021) Metabolomics |  https://doi.org/10.1007/s11306-020-01762-3

Long-term intake of proton pump inhibitors (PPIs) has been associated with elevated risk of cardiovascular events. Nevertheless, they are still widely used to treat increased gastric-acid production, even without medical supervision. Demonstrating and understanding a causal link between PPIs and cardiovascular risk could help to inform regulatory decisions around the safe use of PPIs in future.

It has been suggested that PPIs block the enzyme activity of dimethylarginine dimethylaminohydrolase (DDAH). This leads to increased levels of endothelial asymmetrical dimethylarginine (ADMA), which in turn inhibits endothelial nitric oxide synthase (eNOS) and lowers nitric oxide (NO) levels. Since NO is responsible for endothelial functions, like vasorelaxation, disruption of its regulatory circuit might cause cardiovascular events.

So far, this mechanism of action has been proved only in human cell cultures and mouse models by altered ADMA levels. Based on this, Dr. Baumeister and colleagues in Munich hypothesized an association between decreased levels of citrulline, which is the product of both DDAH and eNOS activity, and daily intake of PPIs.

The study included participants of the population-based Study of Health in Pomerania (SHIP-0 and SHIP-2). The research team analyzed the association of regular long-term intake of PPIs and serum metabolites of the NO pathway. Additionally, the effect of PPIs on flow-mediated vasodilatation (FMD) was assessed.

PPI users showed 0.99% lower FMD than non-users. Similarly, PPI intake resulted in 3.03 µmol/L reduced citrulline levels. These results are quite substantial considering the population mean FMD of 5.59% and citrulline of 31.46 µmol/L. The analysis also showed that to refute these observed associations, unmeasured confounders would have to be related to PPI intake by a risk ratio of 1.9 above those measured. No association was found between PPI intake and the other metabolites of the NO pathway, ADMA, symmetric dimethylarginine (SDMA), and arginine, which is consistent with previous evidence.

Directly assessing the relation between PPI intake and NO levels is challenging because NO is reactive and complex to measure. ADMA does not appear to be a practical indicator of DDAH activity since its plasma levels do not reliably reflect its endothelial levels. Since citrulline is the product of ADMA degradation by DDAH and eNOS activity, it might show the biggest response to PPI intake. Finally, this study confirmed that citrulline could be a suitable marker for disruptions of the NO pathway leading to decreasing NO levels and increasing risk for cardiovascular events.

Explore our recent articles to find out more about how metabolomics in population-based cohort studies can help reveal mechanisms of actions.

Nolde M, Bahls M, Friedrich N, Dörr M, Dreischulte T, Felix SB et al.: Association of proton pump inhibitor use with endothelial function and metabolites of the nitric oxide pathway: A cross-sectional study. (2021) Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy | https://doi.org/10.1002/phar.2504

Up to three quarters of all malaria cases are chronic, which presents a major hurdle for combatting the disease. These chronic cases are often asymptomatic, but can have long term negative health effects for the infected, while serving as source for mosquito-borne transmission. While malaria worldwide still presents a major health concern, the biological mechanisms behind chronic malaria are not well understood.

In a new study by Cordy et al, metabolomics and transcriptomics techniques were used to observe alterations in metabolism between chronic and acute cases of malaria. The goal was to identify metabolites and parasite transcriptional features that could help distinguish the two cases to better understand the biology behind disease progression. To determine the relevance of animal models, the authors monitored macaques infected with Plasmodium coatneyi longitudinally, and compared with human samples infected with Plasmodium falciparum. Throughout the study, the authors observed clinical parameters, plasma metabolomes, and parasite transcriptional profiles.

Untargeted high-resolution mass spectrometry analysis was used to measure and annotate metabolic profiles. Targeted metabolomics was then used to further characterize and quantify clinically relevant metabolites for each type of infection. Metabolomics results showed global changes between both infection types in amino acid, biogenic amine, carnitine, and lipid metabolism, while transcriptomics in macaques revealed alterations in amine, fatty acid, lipid, and energy metabolism.

Targeted, tandem mass spectrometry data also revealed significant perturbations in clinically related metabolic ratios in human samples. The Fischer ratio (liver dysfunction), kynurenine to tryptophan (immunosuppression/tolerance), mono-unsaturated to saturated phosphatidylcholines (fatty acid desaturases activity), and total lyso-phosphatidylcholines to total phosphatidylcholines (phospholipases activity) were all found to be significantly altered, peaking in the acute case where the disease was most active. The metabolic ratio indicators correlated well with the pathway analysis from the untargeted data and demonstrated that both fatty acid metabolism and lipid degradation were elevated during the acute cases.

When looked at together, the authors discovered a large set of metabolites and indicators are differentially and significantly expressed in chronic malaria compared to acute cases. These observed alterations in metabolites may be key to better understanding the biology of host-parasite interactions in malarial disease progression. Further, it was demonstrated that metabolite alterations in the macaques were comparable to those from humans, providing a robust animal model for future studies. The study demonstrates a first attempt at integrating various data types and animal models to provide a wholistic picture for better understanding the dynamics between acute and chronic malaria.

Visit our applications page to learn more about metabolomics in disease and wellbeing.
Learn more about how metabolic indicators can help answer biological questions and provide new data insights with our blog post on MetaboINDICATOR™.

Cordy RJ, Patrapuvich R, Lili LN, Cabrera-Mora M, Chien J, Tharp GK, Khadka M, Meyer EVS, Lapp SA, Joyner CJ, Garcia A, Banton S, Tran V, Luvira V, Rungin S, Saeseu T, Rachaphaew N, Pakala SB, DeBarry JD, MaHPIC Consortium, Kissinger JC, Ortlund EA, Bosinger SE, Barnwell JW, Jones DP, Uppal K, Li S, Sattabongkot J, Moreno A, Galinski MR: Distinct amino acid and lipid perturbations characterize acute versus chronic malaria (2019) JCI Insight | https://doi.org/10.1172/jci.insight.125156

Obesity is a risk factor for a wide range of diseases, from diabetes and cardiovascular problems, to neurodegenerative diseases and various types of cancer. Improving our understanding of the biochemical processes associated with overweight and obesity are therefore important. Better knowledge of how obesity affects disease could help predict individual health outcomes.

Researchers at the University of Milan and the Policlinico of Milan published a new study, which replicates previous findings and improves knowledge of how metabolism is increasingly dysregulated with a growing degree of obesity. The researchers investigated the plasma metabolome of 1391 participants of the SPHERE study, using biocrates’ AbsoluteIDQ® p180 kit. Participants were split into four groups according to Body Mass Index (BMI <30; BMI 30-34.9; BMI 35-39.9; BMI ≥40).

Significant associations between metabolite levels and BMI were detected for dozens of metabolites. Among the positively associated metabolites, the amino acids tyrosine, valine and isoleucine were among the most strongly dysregulated substances. Negatively associated metabolites were found to include a large range of lipids, especially PC ae lipids. Several of the altered metabolites show a clear association with the degree of obesity.

The discussion of results highlights the reproducibility of the chosen metabolomics approach by relating findings to several papers which used the same kit, including a previously published study on reference values in healthy individuals. Probably owing to the relatively large study size, previously unknown associations between selected phosphatidylcholines and obesity have been reported for the first time.

Finally, the study confirms that although metabolite levels are clearly influenced by obesity, the effect varies from person to person. This variability could help predict obesity-related health outcomes, by increasing our understanding of why some obese individuals remain healthy for many decades, while others are affected by myocardial infarction, stroke, chronic kidney disease, colorectal cancer and other diseases.

Several interesting parallels can be drawn between the findings of the SPHERE study and  previous studies that have tried to predict outcomes:

-The majority of lipids found to be inversely associated with coronary heart disease are among the lipids significantly reduced in obesity. (https://biocrates.com/circulating-metabolites-predict-coronary-heart-disease-risk/)
-Kühn et al. (BMC Medicine 2016; 14:13) also found PC ae 30:0 to be negatively correlated with BMI, with higher levels of this lipid associated with an increased risk for common cancers.
-The finding that beta-oxidation of very-long chain fatty acids (VLCFA) is among the pathways strongly affected by obesity may be relevant for cancer immunotherapy: Mock et al. proposed VLCFA as biomarkers for the prediction of response to immune checkpoint inhibitor therapy.(https://biocrates.com/make-sure-your-drug-fits-your-cancer/)
The impact of obesity on response to immune checkpoint inhibitor therapy is also the subject of discussion, albeit with conflicting results.

In summary, this paper highlights the complex relationship between obesity and metabolism, confirming previous findings and finding new associations. The results deepen our knowledge of how obesity is related to chronic diseases and how metabolism defines clinical outcomes.

Frigerio G, Favero C, Savino D et al.: Plasma Metabolomic Profiling in 1391 Subjects with Overweight and Obesity from the SPHERE Study. (2021) Metabolites | https://doi.org/10.3390/metabo11040194

Prenatal exposure to harmful substances is widely accepted as a health risk later in life. Prospective cohort studies starting during pregnancy are a powerful tool to identify risk factors and to develop prevention strategies for diseases.

In a study under the Human Early Life Exposome (HELIX) project, Dr. Nikos Stratakis and colleagues analyzed the effect of prenatal PFAS exposure (exposure to perfluoralkyl substances) on the prevalence of liver injuries in childhood. PFAS are very stable and ubiquitous chemicals used in the industrial production of a wide variety of consumer goods, which considerably accumulate in the human liver and have been shown to exert hepatotoxic effects in animal models.

The study measured PFAS concentration in the maternal blood during pregnancy as well as liver enzyme levels and serum metabolites in the children during follow-up.

Higher PFAS exposure during pregnancy was associated with higher liver enzyme levels in serum, indicating an increased level of liver injuries in these children. Child serum metabolomics revealed perturbations in the amino acid and glycerophospholipid metabolism. A distinct metabolic profile for children at high risk of liver injury was identified. It was characterized by a high PFAS exposure in utero and included elevated levels of branched-chain amino acids, aromatic amino acids, and glycerophospholipids. Abnormal glycerophospholipid concentrations can induce hepatic lipotoxicity and inflammation. Notably, this is a hallmark of non-alcoholic fatty liver disease (NAFLD), which is increasingly diagnosed in children.

As part of the HELIX project, this study provides profound datasets on mother-child pairs from six countries, observing the effect of exposure to environmental PFAS mixtures during the critical time of fetal development. The increasing prevalence of liver injuries like NAFLD in children highlights the importance of the results for public health and prevention policy.

If you are interested in applying metabolomics in population-based studies, go ahead and watch our free virtual symposium with internationally renowned researchers presenting “The future of population health”.

Stratakis N, V Conti D, Jin R, Margetaki K, Valvi D, Siskos AP et al.: Prenatal Exposure to Perfluoroalkyl Substances Associated With Increased Susceptibility to Liver Injury in Children. (2020) Hepatology | https://doi.org/10.1002/hep.31483

Although they are present in relatively low concentration in our bodies, steroid hormones play a crucial role in a vast variety of biological processes, from maintaining pregnancy to regulating mineral and glucose balance in the organism. However, their roles and interactions are still far from being understood. Two recent papers investigating steroid hormones in the KORA cohort shed further light on how these powerful metabolites define health and disease.
Prof. Jerzy Adamski, one of the co-authors of the articles, summarizes the findings and their relevance:

Androgens associate with mortality risk

Although too high or too low levels of androgens are associated with osteoporosis or cardiovascular disorders, there is no established threshold level for steroids in these diseases. Furthermore, there is a need to understand all-cause mortality mechanisms on the metabolic level. Schrederecker et al. studied human serum samples for changes in sex hormone binding globulin (SHGB), testosterone and its active metabolite dihydrotestosterone (DHT) in comparison to survival in a German cross-sectional KORA F4 study. The authors found that higher SHBG concentrations were associated with increased risk of all-cause mortality both in men and women. Lower testosterone and calculated free testosterone as well as higher DHT concentrations in women were associated with increased risk of all-cause mortality. This manuscript contributes to the knowledge that androgens impact survival also in women.

Sex hormones and normoglycemia

Sexual dimorphism is observed in health and disease but the mechanisms underlying different frequencies of disorders are not fully understood. Progestogens and estrogens were considered to be of limited importance to male metabolic homeostasis up to now. However, a recent study by Lau at al. demonstrated that we might have to rethink the importance of these steroids in males. The specific research topic in this project was impact of progestogens and estrogens on normoglycemia in both women and men. The analyses of steroids were performed in the well characterized KORA study cohort and contained both cross-sectional and prospective samples. The mass spectrometry-based assay ensured specific quantification of steroids not blurred by the cross-reactivity of antibody-based assays. The outcome of this study is somewhat surprising. Progesterone, its metabolites 17α-hydroxyprogesterone and 17β-estradiol are independently associated with glycemic traits indeed in men as well as in women.

The researchers involved in the KORA study had already previously addressed the “female health-survival paradox”, i.e. the fact that in industrialized economies, women live longer but also suffer higher rates of serious health challenges. The current studies shed further light on the similarities and differences in how steroid hormones contribute to aging, disease and mortality in men and women. Most importantly, the studies add to our understanding of how progesterons and estrogens influence male health, as well as our understanding of how androgens are involved in aging in women.

Schederecker, F., Cecil, A., Prehn, C. et al.: Sex hormone-binding globulin, androgens and mortality: the KORA-F4 cohort study. (2020) Endocrine connections | https://doi.org/10.1530/EC-20-0080

Lau LHY, Nano J, Cecil A, et al.: Cross-sectional and prospective relationships of endogenous progestogens and estrogens with glucose metabolism in men and women: a KORA F4/FF4 Study. (2021) BMJ Open Diabetes Research and Care | https://doi.org/10.1136/bmjdrc-2020-001951