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Systemic proteome adaptions to 7-day complete caloric restriction in humans

Nature Metabolism volume 6, pages 764–777 (2024)Cite this article

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Abstract

Surviving long periods without food has shaped human evolution. In ancient and modern societies, prolonged fasting was/is practiced by billions of people globally for religious purposes, used to treat diseases such as epilepsy, and recently gained popularity as weight loss intervention, but we still have a very limited understanding of the systemic adaptions in humans to extreme caloric restriction of different durations. Here we show that a 7-day water-only fast leads to an average weight loss of 5.7 kg (±0.8 kg) among 12 volunteers (5 women, 7 men). We demonstrate nine distinct proteomic response profiles, with systemic changes evident only after 3 days of complete calorie restriction based on in-depth characterization of the temporal trajectories of ~3,000 plasma proteins measured before, daily during, and after fasting. The multi-organ response to complete caloric restriction shows distinct effects of fasting duration and weight loss and is remarkably conserved across volunteers with >1,000 significantly responding proteins. The fasting signature is strongly enriched for extracellular matrix proteins from various body sites, demonstrating profound non-metabolic adaptions, including extreme changes in the brain-specific extracellular matrix protein tenascin-R. Using proteogenomic approaches, we estimate the health consequences for 212 proteins that change during fasting across ~500 outcomes and identified putative beneficial (SWAP70 and rheumatoid arthritis or HYOU1 and heart disease), as well as adverse effects. Our results advance our understanding of prolonged fasting in humans beyond a merely energy-centric adaptions towards a systemic response that can inform targeted therapeutic modulation.

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Fig. 1: Study design and core participant characteristics.
Fig. 2: Significantly associated plasma proteins with evidence of tissue enrichment.
Fig. 3: Temporal trajectories of plasma protein levels during fasting.
Fig. 4: Proteins associated with changes in plasma 3-hydroxybutyrate and weight.
Fig. 5: Convergence of genetically predicted and observed effects of proteins on fasting metabolism.
Fig. 6: Protein–disease networks.
Fig. 7: Fasting proteins with a putative causal role in CAD.

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Data availability

GWAS summary statistics for proteins are available from an interactive webserver (https://omicscience.org/apps/olinkpgwas/) and statistics for other outcomes have been obtained from the OpenGWAS database with relevant identifiers listed in Supplementary Table 4 (https://gwas.mrcieu.ac.uk/). Proteomic data has been deposited in ref. 55. Tissue annotations for Olink proteins have been obtained from https://www.proteinatlas.org/.

Code availability

Associated code and scripts are available at https://github.com/comp-med/olink-fasting-study.

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Acknowledgements

We thank M. Valde for excellent technical assistance and organizing blood sampling. The authors acknowledge the Scientific Computing of the IT Division at the Charité, Universitätsmedizin Berlin for providing computational resources that have contributed to the research results reported in this paper (https://www.charite.de/en/research/research_support_services/research_infrastructure/science_it/#c30646061). This work was supported by the Norwegian School of Sport Sciences, the DZHK (German Centre for Cardiovascular Research) and the BMBF (German Ministry of Education and Research). We thank the time and effort of the study participants and investigators of the EPIC-Norfolk study (DOI 10.22025/2019.10.105.00004; https://www.epic-norfolk.org.uk/) whose data have enabled this research. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany

    Maik Pietzner, Burulça Uluvar & Claudia Langenberg

  2. Precision Healthcare University Research Institute, Queen Mary University of London, London, UK

    Maik Pietzner & Claudia Langenberg

  3. MRC Epidemiology Unit, University of Cambridge, Cambridge, UK

    Maik Pietzner & Claudia Langenberg

  4. Department of Physical Performance, Norwegian School of Sport Sciences, Oslo, Norway

    Kristoffer J. Kolnes, Øyvind Skattebo, Egil I. Johansen & Jørgen Jensen

  5. Steno Diabetes Center Odense, Odense University Hospital, Odense, Denmark

    Kristoffer J. Kolnes

  6. Department of Clinical Medicine, Aarhus University, Aarhus, Denmark

    Per B. Jeppesen

  7. Institute of Health and Society, Faculty of Medicine, University of Oslo, Oslo, Norway

    S. Victoria Frivold

  8. Department of Nutrition, Division for Molecular Nutrition, University of Oslo, Oslo, Norway

    Bjørn S. Skålhegg

  9. August Krogh Section for Molecular Physiology, Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark

    Jørgen F. P. Wojtaszewski

  10. Section of Specialized Endocrinology, Department of Endocrinology, Oslo University Hospital, Oslo, Norway

    Anders J. Kolnes

  11. Metabolic Research Laboratory, Wellcome-MRC Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK

    Giles S. H. Yeo & Stephen O’Rahilly

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Contributions

Conceptualization: M.P. and C.L. Data curation/software: M.P., B.U., K.J.K., P.B.J., S.V.F., Ø.S., B.S.S., E.I.J. and A.J.K. Formal analysis: M.P. and B.U. Methodology: M.P., K.J.K., P.B.J., S.V.F., Ø.S. and A.J.K. Visualization: M.P. and B.U. Funding acquisition: C.L. and J.J. Project administration: C.L. and J.J. Supervision: M.P., C.L. and J.J. Writing—original draft: M.P. and C.L. Writing—review and editing: B.U., K.J.K., P.B.J., S.V.F., Ø.S., B.S.S., E.I.J., J.F.P.W., A.J.K., G.S.H.Y. and S.O.

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Correspondence to Maik Pietzner or Claudia Langenberg.

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Nature Metabolism thanks Benjamin Horne, Huiyong Yin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ashley Castellanos-Jankiewicz, in collaboration with the Nature Metabolism team.

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Extended data

Extended Data Fig. 1 Change in body composition as measured by dual-energy X-ray absorptiometry (DEXA).

Each panel contains a separate measure and mean ± SEM are displayed for a change compared to baseline values. n = 12 (individuals) x 3 (timepoints) samples; Corresponding association statistics can be found in Supplementary Table 1. SAT = subcutaneous adipose tissue; VAT = visceral adipose tissue.

Extended Data Fig. 2 Change in urinary nitrogen excretion during the time course of the study.

Each panel contains a separate measure and mean ± SEM are displayed for a change compared to baseline values. n = 12 (individuals) x 7 (timepoints) samples; Corresponding association statistics can be found in Supplementary Table 1.

Extended Data Fig. 3 Individual time courses of selected protein candidates.

The upper panel displays mean ± SEM in original units for each time point of the study, whereas the lower panel displays mean ± SEM for change compared to baseline values. Thin grey lines indicate individual participants. n = 12 (individuals) x 10 (timepoints) samples.

Extended Data Fig. 4 Volcano plot of protein changes.

The y-axis displays corrected p-values from mixed effect linear regression models for a time effect, whereas the x-axis displays the largest extend proteins changed during the study. The size of the dot indicates at which timepoint the largest average change was observed.

Extended Data Fig. 5 Proteins with a sex-differential response during the study period.

Sex-specific mean ± SEM values are shown for three proteins that showed significant evidence (q-value < 0.05) for sex-differential effects. 1 = men; 0 = women. The upper panel displays original values, whereas the lower panel displays changes from baseline. n = 12 (individuals) x 10 (timepoints) samples.

Extended Data Fig. 6 Results from pathway enrichment analysis.

The first box refers to results using all significantly altered proteins, whereas all remaining refer to one of the clusters of proteins shown in main Fig. 2. Each box displays the p-value (x-axis) and fold enrichment (colour intensity) for distinct set of pathways.

Extended Data Fig. 7 Tissue enrichment of proteins altered during fasting.

Plot displays results of Fisher’s exact tests (two-sided) for the enrichment of proteins altered during fasting among tissue-specific proteins, according to Human Protein Atlas. The y-axis shows the odds ratio estimate, the x-axis is ordered by –log10(p-value). The sizes of the dots show the number of proteins that are both tissue-specific and their plasma levels change during fasting, and they have a black border if the Bonferroni-adjusted Fisher’s p-value for 36 tissues is below 0.05.

Extended Data Fig. 8 Cell-type enrichment of proteins altered during fasting.

Same as Extended Data Fig. 7 (two-sided Fisher’s exact test), but for celltype-specific proteins according to Human Protein Atlas. Dots have a black border if the Bonferroni-adjusted Fisher’s p-value for 79 cell types is below 0.05.

Extended Data Fig. 9 Changes in proteins belonging involved in cholesterol metabolism during the study period.

Each protein measured in the current study is coloured according to the trajectory during fasting. The colour gradient is based on effect estimates from linear mixed models and has been restricted to −1 and 1 to enhance visualisation. Created using KEGG Database.

Extended Data Fig. 10 Changes in the complement and coagulation cascade during the study period.

Each protein measured in the current study is coloured according to the trajectory during fasting. The colour gradient is based on effect estimates from linear mixed models and has been restricted to −1 and 1 to enhance visualisation. Created using KEGG Database.

Supplementary information

Reporting Summary

Supplementary Tables 1–4

Contains Supplementary Tables 1–4.

Supplementary Data 1

Study protocol.

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Pietzner, M., Uluvar, B., Kolnes, K.J. et al. Systemic proteome adaptions to 7-day complete caloric restriction in humans. Nat Metab 6, 764–777 (2024). https://doi.org/10.1038/s42255-024-01008-9

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