Completed Diabetes, Hormones & Metabolism Cells, Biochemistry & Physiology

UK Consortium for MetAbolic Phenotyping (MAP UK)

In plain English

AI plain-English summary

A person’s urine or blood contains thousands of chemical compounds that together form a unique metabolic “fingerprint,” and this consortium will build a national platform to help UK scientists read and use those fingerprints to predict, diagnose, and treat disease. Metabolic phenotyping—measuring the full chemical profile of biofluids and tissues—can reveal how genes, diet, lifestyle, and environmental exposures combine to shape an individual’s health. But the field is fragmented: clinicians, chemists, and data scientists each use different tools and speak different languages. This consortium brings together leading UK research institutions to create a shared, standardised infrastructure for metabolic measurement and data analysis. If successful, the project will give UK researchers easy access to advanced phenotyping tools that are currently scattered across specialist labs. This could accelerate stratified medicine—matching patients to the right therapy based on their metabolic profile—and improve early detection of diseases such as diabetes, cardiovascular conditions, and metabolic disorders. The consortium will also train researchers across disciplines, raising the quality and efficiency of human health research nationwide.

View original technical description
Metabolic phenotyping is the study of human health by observation of the chemical composition of biofluids (e.g. urine and blood) and tissues. Advanced technologies are used to measure the amounts of thousands of metabolites in human samples in an unbiased "untargeted" manner, generating individual profiles (or chemical "fingerprints") that represent the individual's metabolic phenotype. A phenotype is the product of a person's genes and the influence of numerous environmental factors including diet, lifestyle, occupation, stress, exercise, and exposures (e.g. to medications, toxins, pollutants, etc). A phenotype can be understood and measured by direct measurement of a person's metabolites, either in circulation (e.g. in blood), localised to a tissue (e.g. a muscle biopsy), or as the end products of metabolism (e.g. urine). Metabolic phenotyping is key as an emerging technology used to increase our understanding of human health and disease, with applications in the prediction of disease predisposition, onset, and potential for recovery, as well as the power to guide personalised therapies and clinical interventions where needed (a field called "stratified medicine"). To be used to its full potential, metabolic phenotyping requires collaboration among many specialist areas of science. Clinicians, epidemiologists, and other scientists who wish to use phenotyping tools to investigate specific scientific questions (related to health in individuals, populations, or the underlying chemical mechanisms of diseases) must work together with technologists and analytical chemists who understand biofluid handling and measurement, data scientists (e.g. bioinformaticians) who can make sense of large amounts of complex data, and biochemists who can interpret that data. With many different ways to raise hypotheses, design studies, measure biofluids, and analyse data, metabolic phenotyping is a field of specialists who use specialist tools to answer specific questions. While it is not practical for one person to master all of these disciplines, an appreciation for the whole process is vital to ensure efficient use of powerful technologies and precious human samples, and communication among specialists is vital to achieving positive and impactful research outcomes. With this in mind, we aim to advance metabolic phenotyping for the benefit of UK scientists by driving cooperation, collaborative development, and education within the framework of a partnership among leading UK research institutions. We will organise and share specialist methods for metabolic measurement and data analysis among expert technologists, analytical chemists, and data scientists, as well as improve the visibility and availability of these tools to the UK research community. We will start our programme by listening to the UK research community, identifying their phenotyping needs and matching them to potential technological/methodological solutions held within our partnership. We will ensure the availability of these solutions by cross-site training, developing a strong network of research capability and capacity within the UK. Where gaps in our collective abilities exist, analytical and informatics development programmes will work to address those specific needs as well as maximise the obtainable value from phenotyping data. We will test and demonstrate our collective abilities by turning this newly organised resource toward the study of example applications, and we will conclude our programme by presenting our work and harmonised platform for phenotyping back to the UK research community. We will augment this cycle with external training of all user groups to raise awareness for the strengths, limitations, specific considerations and diversity of phenotyping approaches, enabling more efficient, more impactful, and higher quality human health and disease research throughout the UK.

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Researchers

Baukje De Roos (Co-Investigator)John Draper (Co-Investigator)Julian Griffin (Co-Investigator)Mark Viant (Co-Investigator)Matthew Lewis (Co-Investigator)Royston Goodacre (Co-Investigator)Toru Suzuki (Co-Investigator)Valerie O'Donnell (Co-Investigator)Warwick Dunn (Co-Investigator)Zoltan Takats (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

PhenoMeNal: A comprehensive and standardised e-infrastructure for analysing medical metabolic phenotype data
Unifying metabolome and proteome informatics
Bioinformatics for spatial metabolomics
Closing the gaps in metabolomics - Identifying unknown metabolites and mapping onto biochemical pathways
Harmonising and Unifying Blood Metabolomic Analysis Networks (HUMAN)

Original classification

Research Grant

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