Over the past decade, the field of metabolism has witnessed remarkable scientific discoveries that reshaped the understanding of metabolic physiology and disease. As we launch Nature Metabolism, we look at what the future holds for metabolic research.

From single-cell analyses to DNA and RNA editing to large-scale genomic sequencing and multi-omics approaches in combination with big data analysis and machine learning, new technologies bring us closer than ever to understanding the causes of metabolic diseases, developing therapies against them, elucidating the metabolic underpinnings of cancer and stem cell proliferation, discovering novel metabolic pathways and revealing the biological principles of ageing. As a forum for cutting-edge metabolism research, Nature Metabolism joins this community with ambition to shape the field going forward. Curious as to what we might expect on our journey, we asked ten experts, working in different subfields of metabolic research, about the key developments that they think will shape their fields over the next decade. Here are their responses, in alphabetical order.

Fredrik Bäckhed: Microbiome research

Research over the past 15 years has revealed how the gut microbiota—the collective assembly of microorganisms residing in the gut—influences host physiology and metabolism through interactions with our diet and the modulation of functions in peripheral organs. However, most clinical studies so far have been cross-sectional, leading to the identification of associations between disease markers in patients and specific bacterial taxa. In addition, few findings have been reproduced because many of these studies have been underpowered. One consistent finding has been that obese humans tend to have reduced microbial diversity, which may reflect reduced dietary intake of fibres compared with healthy individuals. By contrast, fibre-degrading butyrate-producing bacteria are reduced in type 2 diabetes. As diet and medication are known to have a great impact on the microbiota, it will be imperative to study larger cohorts of patients who are not taking drugs. Prospective studies are needed to determine whether an altered microbiota is observed prior to disease development. Although the microbiota has been causally linked to disease in animal models, similar links have yet to be demonstrated in humans, who have been the subjects of only few such studies. Finally, future work should focus on identifying mechanisms that are relevant to metabolic diseases in humans in order to achieve the major overall aim of the field: developing microbiota-based therapeutics for metabolic diseases.

Elisabetta Bugianesi: Non-alcoholic fatty liver disease

The global epidemic of obesity is reshaping the epidemiology of chronic liver diseases, producing a dramatic increase of non-alcoholic fatty liver disease (NAFLD) that may ultimately progress to non-alcoholic steatohepatitis (NASH), advanced fibrosis, cirrhosis and liver cancer. NASH and significant fibrosis are harbingers of adverse hepatic outcomes, but NAFLD is a complex disease with clinical outcomes that extend beyond the liver to include cardiovascular disease (CVD). Approximately 30% of adults and 10% of children in Western countries have NAFLD. In the United States, prevalent NAFLD cases are forecasted to increase by 21%, from 83.1 million to 100.9 million, while prevalent NASH cases will increase by 63%, from 16.52 million to 27 million cases, between 2015 and 2030. This translates into a dramatic social and economic burden. The epidemiological association of NAFLD with diabetes, CVD and stroke is well-known, but the understanding of the pathogenic mechanisms linking the parallel progressions of hepatic damage and CVD risk remains incomplete. In addition, substantial inter-individual variability in liver-related morbidity versus the extent of co-morbidities exists together with an unmet need to identify patients with NAFLD at early stages of the disease to prioritize preventive and therapeutic strategies. To date, the lack of non-invasive markers of hepatic damage has hampered risk stratification in NAFLD and limited drug development, but a recent surge of biomarkers and therapies holds promise for the future. Importantly, there is a need for scientists working in the fields of obesity, diabetes and CVD to cooperate by sharing large databases and biological samples. This will allow identification and assessment of the complex phenotypes of patients with NAFLD by integrative systems-biology approaches, which will therefore lead to personalised medical care.

Heather Christofk: Cancer and stem cell metabolism

Recent studies have shown that changes in diet-derived nutrients, including amino acids and vitamins, can impact cancer growth and response to therapy. Over the next 10 years, a major focus in the metabolism field will be on understanding how specific nutrients influence disease progression and treatment, as well as how this knowledge can be applied to understand mechanisms of normal development. For instance, whether and how diet-regulated nutrients alter fate decisions of adult stem cells will provide insight into the currently unexplained variability in propensity to acquire diseases such as cancer, neurological disorders, metabolic conditions, and others. Additionally, how diet-regulated nutrients influence developmental processes should be re-examined with the modern aim of unravelling mechanistic connections between specific metabolites, cell signalling pathways, and epigenetic state. The implications for these types of studies are vast: understanding how diet-regulated nutrients impact disease progression will enable future use of dietary strategies to improve treatments as well as promote nutritional awareness through the modern lens of health and disease for future generations.

Ivan Dikic: Cellular stress and proteostasis

Cellular metabolism is part of a comprehensive network of many physiological processes that also wires responses to stress conditions during the development of human diseases. In order to investigate all of these interlocked processes and how they influence each other, end products, intermediates of metabolic pathways and the activity of key enzymes first need to be measured in vivo. Next, it will be crucial to model the contributions of the different metabolic pathways on specific cellular functions. In particular, the role of proteins, nucleotides, sugars, lipids and other metabolites or natural compounds on quality-control pathways will have pivotal relevance in biomedicine. Notably, the proteasome and autophagosome, the two major degradation systems that deal with damaged proteins, protein aggregates and dysfunctional organelles are also major sources of various metabolites. In addition, both cellular waste disposal and stress responses influence metabolism by degrading components or regulators of metabolic pathways. Vice versa, metabolites and intermediates can directly affect proteostasis and cellular homeostasis by activating or inhibiting factors that control proteasomal and autophagic activity. Understanding the crosstalk of the proteasome and autophagy as well as their interaction with metabolic pathways will therefore be a prominent topic in the future. Particularly challenging will be the development of sensitive techniques to faithfully monitor the activities of the two degradation systems in mice and patient samples and their integration with metabolomics, lipidomics and proteomics data. These questions will affect multiple clinical areas, such as oncology, neurology and metabolic diseases.

Rana Gupta: Adipose tissue biology and obesity

Mammals evolved an extraordinary ability to adapt to changes in energy demand and nutrient availability, and it has been discovered that adipose tissue is more complex and flexible than what was initially thought. It has recently been discovered that adipocytes can drive energy expenditure in uncoupling protein-1 (UCP1)-independent ways and utilise factors such as lipokines, microRNAs and other metabolites to communicate with neighbouring cells or distal tissues. In addition, fat cells have long been designated as white or brown, but it is now recognised that beige adipocytes are a distinct type of adipocyte and that anatomically distinct white adipocytes differ significantly from one another. Single-cell sequencing studies have shown that adipocyte progenitors are also heterogeneous and that the idea of a single white or brown preadipocyte is too simplistic. The concept of the adipocyte as a terminally differentiated cell may also be changing with evidence that adipocytes can revert to a preadipocyte-like phenotype. I suspect that the heterogeneity and plasticity of adipose tissue will be important themes over the next 10 years and that the field will aim to identify all the different types of adipocytes and their progenitors and investigate their differences. Understanding the function of these cell types and how or whether distinct subpopulations can differentiate into other cell types will be crucial. Ultimately, we will need a new set of biochemical and genetic tools to isolate and/or target individual subpopulations of adipocytes and progenitors in a depot-specific manner. Such complexity can be daunting, but achieving this will grant opportunity to develop new strategies and therapeutics for metabolic disease.

William Mair: Ageing

The 20th century saw a phenomenal increase in human life expectancy, driven by public health successes that primarily reduced infant mortality and infectious disease. Yet this success has come at some cost: levels of co-morbidities in the elderly never before seen and unsustainable burdens upon healthcare systems. ‘Geroscience’ aims to first understand and then to target the central processes that are deregulated with age, so that years added to the human lifespan are transformed into healthier ones. Research has progressed from uncovering single-gene modulations that extend lifespan in invertebrates to conservation of those effects in mammals and genome-wide association study links to extreme longevity in humans. Finally, pro-longevity interventions based on small molecules are now in clinical trials for assessment of their effects on all-cause morbidity in the elderly, with the goal of making anti-ageing benefits themselves an FDA-approved outcome. The field has come a long way. However, focus on lifelong interventions that increase median lifespan may have limited potential for translation to usable human therapeutics. Invariably, within long-lived populations reside individuals who show no increased or reduced longevity compared with controls. In addition, for dietary, genetic and pharmacological interventions that extend lifespan, both optimal dose and efficacy of effect are often vastly different across alternative wild-type backgrounds and between sexes. As the filed moves towards translation to therapeutics, a precision-medicine approach to ageing biology is needed, shifting the focus from biomarkers of biological age to those that predict response to treatment. With this goal in mind, an understanding of the basic biology of ageing might move the field towards the day when optimal intervention and dose can be predetermined at the level of the individual, maximising healthy ageing for all.

Luke O’Neill: Immunometabolism

The past 10 years has seen a remarkable advance in the understanding of metabolic changes in immune cells. Metabolism has firmly entered immunology as a key process in the explanation of immune response. It is now possible to phenotype different immune cells on the basis of their metabolic profiles. This will continue, and more and more detail emerging on alterations in metabolic pathways will be documented. Hitherto unexplored pathways will be shown to have roles in immune response, with amino acid and possibly purine and pyrimidine metabolism being a particular focus. A key issue, however, will be uncovering how these changes link to specific functional responses in molecular detail, which remains somewhat lacking. Clearly we have learnt that such changes are not only connected to energy metabolism and standard biosynthetic processes. Evolutionary aspects of metabolic changes will remain a question, considering that the dual-purposing of well-known metabolic pathways makes evolutionary sense. The interface between nutrition, the microbiome and immunometabolism will be a particular focus. A final aspect that will be pursued in detail is the therapeutic potential of targeting the metabolic processes that are reported to be involved in inflammatory and infectious diseases, and also expanding such therapies into oncology. Metabolism has been studied in detail in tumours, and its therapeutic potential was first explored in the context of cancer. We can look forward to more exciting discoveries and perhaps ultimately to new treatments for multiple diseases.

Markus Ralser: Omics technologies in metabolism

‘If you understand a system, you can predict it’. Recent advances in computer science have allowed this popular phrase to be extended: ‘If you have lots of data, you can predict a system, even if you don’t understand it’. I expect the field of metabolic research to reach a point within the next decade in which the tide can be turned and that understanding of metabolism will improve because of increasing ability to predict it. The conservation of metabolic networks and the ability to identify enzymes through sequence homology enables the semi-automatic reconstruction of genome-scale metabolic models, ranging from microbes to human cell types. But even with the metabolome, transcriptome and proteome of a cell available, so far it has been a struggle to predict basic metabolic parameters, such as metabolite concentrations and metabolic fluxes, and even more challenging is inferring more complex metabolic parameters, including metabolic interactions between cells, the stress tolerance of a cell and the metabolic composition of cellular components. Recent progress in ‘data-driven’ approaches has, however, changed the situation and proved successful in predicting the metabolome. In order to interpret these predictions, we will need better computational methods and datasets that are both larger and more precise. ‘Understanding by predicting’ will advance metabolism research in an area in which biochemistry has its limits: the dissection of large, multifactorial processes in which many components of different natures act together within the crowded environment of the cell. The advent of artificial intelligence will certainly be a game changer in tackling the complexity of metabolic networks.

David Sabatini: Nutrient sensing and signalling

In the coming years, the field will move in several new directions, some of which reflect general trends in the field of metabolism. Over the last decade, research has uncovered the mechanisms through which mTORC1—a major growth regulator—senses nutrients and drives anabolism. A key challenge is moving the largely in vitro work in vivo, and another is understanding why mTORC1 senses specific nutrients (various amino acids as well as glucose) and the importance in animal physiology of recently discovered nutrient sensors. From a more metabolism-centric point of view, I am excited about two new directions, both of which involve relatively new technologies. The first is untargeted metabolite profiling, with which one can identify metabolites in biological samples without a priori knowledge about their structures. Recent improvements in mass spectrometry as well as analysis software have greatly increased the value of untargeted approaches. These trends will only continue, allowing metabolomics to inch closer to the comprehensiveness possible with proteomics and transcriptomics. The second direction is the study of compartment-specific metabolism that will allow investigators to uncover processes that are not readily detectable at the whole-cell level. In order to achieve this, a rapid isolation of cellular organelles is needed. In our lab we have developed some methods, which in combination with metabolomics, helped us to identify novel metabolic pathways.

Matthias Tschöp: Diabetes

Almost 100 years after the discovery of insulin, diabetes has become a bigger threat to societal health than ever before. While the mechanistic understanding of the exact factors driving this disease remains incomplete, it is clear that without the worldwide spread of obesity, there would be no type 2 diabetes pandemic. Following decades of failed attempts to prevent or cure obesity, recent scientific breakthroughs indicate that change is on the horizon. For example, molecular combinations of two or three endogenous hormones in a single chemical entity have great potential to decrease body fat and regain metabolic control in clinical trials. However, more drug candidates are needed to ensure that the obesity and type 2 diabetes epidemics will be stopped within our lifetime. To achieve this, interdisciplinary programs need to be combined under one roof. The latest enabling technologies must be applied to curiosity-driven basic research as well as to translational metabolic health programs. With public–private partnerships and global cooperation, the necessary acceleration of research to deliver transformative solutions appears within reach. In particular, emerging artificial intelligence and machine-learning tools have the potential to dramatically accelerate the otherwise still rather sluggish process of drug discovery and development. With a bit of luck, the coming decade may become obesity’s last.

Author information


  1. Department of Molecular and Clinical Medicine/Wallenberg Laboratory, Institute of Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden

    • Fredrik Bäckhed
  2. Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

    • Fredrik Bäckhed
  3. Department of Medical Sciences, University of Torino, Corso Dogliotti, Torino, Italy

    • Elisabetta Bugianesi
  4. Department of Biological Chemistry, Department of Molecular and Medical Pharmacology, University of California, Los Angeles, Los Angeles, CA, USA

    • Heather Christofk
  5. Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA

    • Heather Christofk
  6. Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA, USA

    • Heather Christofk
  7. Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA

    • Heather Christofk
  8. Institute for Biochemistry II and Frankfurt Cancer Institute, Goethe University, Frankfurt, Germany

    • Ivan Dikic
  9. Touchstone Diabetes Center, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

    • Rana Gupta
  10. Department of Genetics and Complex Diseases, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    • William B. Mair
  11. School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College, Dublin, Ireland

    • Luke A. J. O’Neill
  12. Molecular Biology of Metabolism Laboratory, Francis Crick Institute, London, UK

    • Markus Ralser
  13. Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK

    • Markus Ralser
  14. Department of Biochemistry, Charité—Universitätsmedizin Berlin, Berlin, Germany

    • Markus Ralser
  15. Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    • David M. Sabatini
  16. Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    • David M. Sabatini
  17. Koch Institute for Integrative Cancer Research and Department of Biology, Massachusetts Institute of Technology, Department of Biology, Cambridge, MA, USA

    • David M. Sabatini
  18. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA

    • David M. Sabatini
  19. Institute of Diabetes and Regeneration Research, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Neuherberg, Germany

    • Matthias Tschöp
  20. Division of Metabolic Diseases, Department of Medicine, Technische Universität München, Munich, Germany

    • Matthias Tschöp


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