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Fasting-mimicking diet prevents high-fat diet effect on cardiometabolic risk and lifespan

Nature Metabolism volume 3, pages 1342–1356 (2021)Cite this article

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Abstract

Diet-induced obesity is a major risk factor for metabolic syndrome, diabetes and cardiovascular disease. Here, we show that a 5-d fasting-mimicking diet (FMD), administered every 4 weeks for a period of 2 years, ameliorates the detrimental changes caused by consumption of a high-fat, high-calorie diet (HFCD) in female mice. We demonstrate that monthly FMD cycles inhibit HFCD-mediated obesity by reducing the accumulation of visceral and subcutaneous fat without causing loss of lean body mass. FMD cycles increase cardiac vascularity and function and resistance to cardiotoxins, prevent HFCD-dependent hyperglycaemia, hypercholesterolaemia and hyperleptinaemia and ameliorate impaired glucose and insulin tolerance. The effect of monthly FMD cycles on gene expression associated with mitochondrial metabolism and biogenesis in adipocytes and the sustained ketogenesis in HFCD-fed mice indicate a role for fat cell reprogramming in obesity prevention. These effects of an FMD on adiposity and cardiac ageing could explain the protection from HFCD-dependent early mortality.

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Fig. 1: The FMD increases longevity and limits body fat accumulation and weight gain.
Fig. 2: FMD cycles improve health biomarkers in HFCD-fed mice.
Fig. 3: FMD cycles rewire metabolism for preferential utilization of fat, lowering calorie intake in HFCD-fed mice.
Fig. 4: FMD cycles improve histological and physiological markers of cardiac health in HFCD-fed mice.
Fig. 5: Restoration of mitochondrial energy metabolism gene expression by the FMD.
Fig. 6: FMD-mediated restoration of physiological and metabolic parameters in HFCD-fed mice.

Data availability

RNA-seq data have been deposited in NCBI’s Gene Expression Omnibus55 under the accession code GSE163060. Source data are provided with this paper.

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Acknowledgements

Funding was provided by the USC Edna Jones chair fund and NIH P01 AG055369-01 to V.D.L. This work was also funded, in part, by the Intramural Research Program of the National Institutes of Health/NIA. We acknowledge support from the USC Molecular Imaging Center and the USC Leonard Davis School Aging Murine Phenotyping Core Facility. A. Mouton was supported by a QCB Collaboratory Postdoctoral Fellowship (UCLA). We used computational and storage services associated with the Hoffman2 Shared Cluster provided by the UCLA Institute for Digital Research and Education’s Research Technology Group.

Author information

Author notes

  1. These authors contributed equally: Amrendra Mishra, Hamed Mirzaei, Novella Guidi.

Authors and Affiliations

  1. Longevity Institute and Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA

    Amrendra Mishra, Hamed Mirzaei, Novella Guidi, Gerardo Navarrete, Min Wei, Sebastian Brandhorst, Stefano Di Biase, Todd E. Morgan & Valter D. Longo

  2. International Clinical Research Center, St Anne’s University Hospital, Brno, Czech Republic

    Manlio Vinciguerra

  3. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA

    Alice Mouton

  4. Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USA

    Marina Linardic & Matteo Pellegrini

  5. Section of Human Anatomy, Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University of Palermo, Palermo, Italy

    Francesca Rappa & Rosario Barone

  6. Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    S. Ram Kumar

  7. Molecular Imaging Center, Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Peter S. Conti

  8. Translational Gerontology Branch, Intramural Research Program of the National Institute on Aging, National Institutes of Health, Baltimore, MD, USA

    Michel Bernier & Rafael de Cabo

  9. IFOM, FIRC Institute of Molecular Oncology, Milano, Italy

    Valter D. Longo

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Contributions

V.D.L. conceived the original idea and supervised the project. V.D.L., M.W., T.E.M. and H.M. designed mouse experiments. H.M., S.B. and S.D.B. collected tissue. G.N. prepared the mouse FMD. M.V., F.R. and R.B. processed and performed immunohistochemistry and quantitative analysis. H.M. performed alanine transaminase, ketone body and serum cholesterol quantification. A. Mishra performed quantification of blood glucose, triglycerides, leptin and ghrelin and the GTT and ITT. N.G. designed and analysed the metabolic cage experiment. N.G. and G.N. performed the metabolic cage experiment. H.M. and S.R.K. performed the stress echocardiography experiment; S.R.K. analysed echocardiography data. V.D.L., N.G. and M.P. designed the RNA-seq experiment. A. Mishra, A. Mouton and M.L. analysed RNA-seq data and made figures. T.E.M., M.W. and P.S.C. were involved in study design. A. Mishra prepared the figures and wrote the initial draft. A. Mishra and V.D.L. revised the manuscript with input from all authors. R.d.C. and M.B. contributed to interpretation, review and final editing.

Corresponding author

Correspondence to Valter D. Longo.

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Competing interests

V.D.L. has equity interest in L-Nutra, which develops and sells medical food for the prevention and treatment of diseases. V.D.L. has committed all his equity in the company to charitable organizations. All other authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Leonie Heilbronn, Satchidananda Panda and William Sessa for their contribution to the peer review of this work. Primary handling editor: Christoph Schmitt.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Project Outline and serum ALT measurement.

Extended Data Fig. 1, related to Fig. 1. Project Outline and serum ALT measurement. a) Schematic representation of the outline of the project. b) Timeline for procedures and functional tests. c) Serum ALT activity to assay liver function at the end of 10 cycles, fourteen days after refeeding. One-way ANOVA, p = 0.1625 ns. Control (n = 6), HFCD (n = 3), HFCD + FMD (n = 4). Data are shown as the mean ± SEM.

Source data

Extended Data Fig. 2 Liver weight and histological assessment after 11 cycles of FMD.

Extended Data Fig. 2 related to Fig. 2. Liver weight and histological assessment after 11 cycles of FMD. a) Liver weight. One-way ANOVA, p = 0.0010***. Tukey’s multiple comparisons tests: Control vs. HFCD, p = 0.0399 *; Control vs. HFCD + FMD, p = 0.0007 ***; HFCD vs. HFCD + FMD, p = 0.2549 ns. Control (n = 6), HFCD (n = 4), HFCD + FMD (n = 6). Data are shown as the mean ± SEM. b) Liver weight to body weight ratio. One-way ANOVA, p = 0.0061. Tukey’s multiple comparisons tests: Control vs. HFCD, p = 0.0404 *; Control vs. HFCD + FMD, p = 0.4297 ns; HFCD vs. HFCD + FMD, p = 0.0048 **. Control (n = 6), HFCD (n = 4), HFCD + FMD (n = 6). Data are shown as the mean ± SEM. c) Liver histological score. One-way ANOVA, p = 0.2314 ns. Control (n = 12), HFCD (n = 11), HFCD + FMD (n = 8). Data are shown as the mean ± SEM. d) Steatosis score. One-way ANOVA, p = 0.4541 ns. Control (n = 12), HFCD (n = 11), HFCD + FMD (n = 8). Data are shown as the mean ± SEM.

Source data

Extended Data Fig. 3 Collagen I and collagen III staining and quantification in aorta.

Extended Data Fig. 3 related to Fig. 4. Collagen I and collagen III staining and quantification in aorta. a) Representative image of Collagen I immunostaining in aorta. Collagen I (green), DAPI (blue). b) Immunofluorescence quantification of Collagen I expression. One-way ANOVA, p = 0.2549 ns. c) Representative image of Collagen III immunostaining in aorta. Collagen III (green), DAPI (blue). d) Immunofluorescence quantification of Collagen III expression. One-way ANOVA, p = 0.0285 *. Tukey’s multiple comparisons tests; Control vs. HFCD, p = 0.0320 *, Control vs. HFCD + FMD, p = 0.8694 ns, HFCD vs. HFCD + FMD, p = 0.0830 ns. Data are shown as mean ± SEM, n = 6 mice per group.

Source data

Extended Data Fig. 4 Stress-echocardiography analysis during the 12th cycle of FMD.

Extended Data Fig. 4 related to Fig. 4. Stress-echocardiography analysis during the 12th cycle of FMD. a) Recovery time from the stress echocardiography measured in min (time taken to return to normal echo). One-way ANOVA, p = 0.4641 ns. b) End Diastolic Volume (EDV). One-way ANOVA, p = 0.8580 ns. c) Left ventricle internal diameter-diastolic (LVID-d). One-way ANOVA, p = 0.8683 ns. d) Interventricular septal thickness-diastolic. One-way ANOVA, p = 0.7956 ns. e) Left ventricle posterior wall thickness-diastolic (LVPW-d). One-way ANOVA, p = 0.4899 ns. f) Left ventricle mass (LV mass). One-way ANOVA, p = 0.9459 ns. Data are shown as mean ± SEM, n = 6 mice per group.

Source data

Extended Data Fig. 5 KEGG and Wikipathways analyses of differentially expressed genes.

Extended Data Fig. 5 related to Fig. 5. KEGG and Wikipathways analyses of differentially expressed genes. KEGG and WikiPathways analyses of differentially expressed genes (DEGs) present in the heart (a) and visceral adipose tissue (b) in Control vs. HFCD (BD) pairwise comparison but absent in Control vs. HFCD + FMD (BC). Adjusted p-value < 0.05.

Source data

Extended Data Fig. 6 Heatmaps representing gene expression levels of the genes identified by KEGG and Wikipathways analyses.

Extended Data Fig. 6 related to Fig. 5. Heatmaps representing gene expression levels of the genes identified by KEGG and Wikipathways analyses. a,b) Heatmaps representing the levels of DEGs present in the heart (a) and adipose visceral adipose tissue (b, c) in the Control vs. HFCD (BD) pairwise comparison but absent in Control vs. HFCD + FMD (BC). DEG enrichment in select pathways as identified by KEGG and WikiPathways analyses. Adjusted p-value < 0.05 & abs(logFC) >1.0 (fold change>2).

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2 and Supplementary Fig. 1.

Reporting Summary

Supplementary Data

Normalized z scores for Supplementary Fig. 1.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Normalized z scores and gene ontology output.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

KEGG and WikiPathways outputs.

Source Data Extended Data Fig. 6

Normalized z scores.

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Mishra, A., Mirzaei, H., Guidi, N. et al. Fasting-mimicking diet prevents high-fat diet effect on cardiometabolic risk and lifespan. Nat Metab 3, 1342–1356 (2021). https://doi.org/10.1038/s42255-021-00469-6

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