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Persistent microbiome alterations modulate the rate of post-dieting weight regain

Abstract

In tackling the obesity pandemic, considerable efforts are devoted to the development of effective weight reduction strategies, yet many dieting individuals fail to maintain a long-term weight reduction, and instead undergo excessive weight regain cycles. The mechanisms driving recurrent post-dieting obesity remain largely elusive. Here we identify an intestinal microbiome signature that persists after successful dieting of obese mice and contributes to faster weight regain and metabolic aberrations upon re-exposure to obesity-promoting conditions. Faecal transfer experiments show that the accelerated weight regain phenotype can be transmitted to germ-free mice. We develop a machine-learning algorithm that enables personalized microbiome-based prediction of the extent of post-dieting weight regain. Additionally, we find that the microbiome contributes to diminished post-dieting flavonoid levels and reduced energy expenditure, and demonstrate that flavonoid-based ‘post-biotic’ intervention ameliorates excessive secondary weight gain. Together, our data highlight a possible microbiome contribution to accelerated post-dieting weight regain, and suggest that microbiome-targeting approaches may help to diagnose and treat this common disorder.

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Figure 1: Enhanced recurrent weight gain after treatment of obesity.
Figure 2: Persistent microbiome alterations after weight loss.
Figure 3: Post-dieting microbiome alterations drive exacerbated weight regain.
Figure 4: Accurate prediction of post-dieting weight regain by microbiota features.
Figure 5: Microbiome modulation ameliorates post-dieting weight regain.
Figure 6: Metabolite modulation ameliorates post-dieting weight regain.

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References

  1. Stevens, J., Oakkar, E. E., Cui, Z., Cai, J. & Truesdale, K. P. US adults recommended for weight reduction by 1998 and 2013 obesity guidelines, NHANES 2007-2012. Obesity (Silver Spring) 23, 527–531 (2015)

    Article  Google Scholar 

  2. Anastasiou, C. A., Karfopoulou, E. & Yannakoulia, M. Weight regaining: from statistics and behaviors to physiology and metabolism. Metabolism 64, 1395–1407 (2015)

    Article  CAS  Google Scholar 

  3. Pietiläinen, K. H., Saarni, S. E., Kaprio, J. & Rissanen, A. Does dieting make you fat? A twin study. Int. J. Obes. 36, 456–464 (2012)

    Article  Google Scholar 

  4. Neumark-Sztainer, D. et al. Obesity, disordered eating, and eating disorders in a longitudinal study of adolescents: how do dieters fare 5 years later? J. Am. Diet. Assoc. 106, 559–568 (2006)

    Article  Google Scholar 

  5. Saarni, S. E., Rissanen, A., Sarna, S., Koskenvuo, M. & Kaprio, J. Weight cycling of athletes and subsequent weight gain in middleage. Int. J. Obes. 30, 1639–1644 (2006)

    Article  CAS  Google Scholar 

  6. Dulloo, A. G. & Montani, J. P. Pathways from dieting to weight regain, to obesity and to the metabolic syndrome: an overview. Obes. Rev. 16 (Suppl 1), 1–6 (2015)

    Article  Google Scholar 

  7. Mehta, T., Smith, D. L., Jr, Muhammad, J. & Casazza, K. Impact of weight cycling on risk of morbidity and mortality. Obes. Rev. 15, 870–881 (2014)

    Article  CAS  Google Scholar 

  8. Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)

    Article  ADS  Google Scholar 

  9. Ridaura, V. K. et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science 341, 1241214 (2013)

    Article  Google Scholar 

  10. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015)

    Article  CAS  ADS  Google Scholar 

  11. David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014)

    Article  CAS  ADS  Google Scholar 

  12. Liu, J., Lee, J., Salazar Hernandez, M. A., Mazitschek, R. & Ozcan, U. Treatment of obesity with celastrol. Cell 161, 999–1011 (2015)

    Article  CAS  Google Scholar 

  13. Elinav, E. et al. Pegylated leptin antagonist is a potent orexigenic agent: preparation and mechanism of activity. Endocrinology 150, 3083–3091 (2009)

    Article  CAS  Google Scholar 

  14. Shpilman, M. et al. Development and characterization of high affinity leptins and leptin antagonists. J. Biol. Chem. 286, 4429–4442 (2011)

    Article  CAS  Google Scholar 

  15. Braune, A. & Blaut, M. Bacterial species involved in the conversion of dietary flavonoids in the human gut. Gut Microbes 7, 216–234 (2016)

    Article  CAS  Google Scholar 

  16. Myoung, H. J., Kim, G. & Nam, K. W. Apigenin isolated from the seeds of Perilla frutescens britton var crispa (Benth.) inhibits food intake in C57BL/6J mice. Arch. Pharm. Res. 33, 1741–1746 (2010)

    Article  CAS  Google Scholar 

  17. Guo, X., Liu, J., Cai, S., Wang, O. & Ji, B. Synergistic interactions of apigenin, naringin, quercetin and emodin on inhibition of 3T3–L1 preadipocyte differentiation and pancreas lipase activity. Obes. Res. Clin. Pract. 10, 327–339 (2016)

    Article  Google Scholar 

  18. Assini, J. M. et al. Naringenin prevents obesity, hepatic steatosis, and glucose intolerance in male mice independent of fibroblast growth factor 21. Endocrinology 156, 2087–2102 (2015)

    Article  CAS  Google Scholar 

  19. Hoek-van den Hil, E. F. et al. Direct comparison of metabolic health effects of the flavonoids quercetin, hesperetin, epicatechin, apigenin and anthocyanins in high-fat-diet-fed mice. Genes Nutr. 10, 469 (2015)

    Article  Google Scholar 

  20. Goldwasser, J. et al. Transcriptional regulation of human and rat hepatic lipid metabolism by the grapefruit flavonoid naringenin: role of PPARα, PPARγ and LXRα. PLoS One 5, e12399 (2010)

    Article  ADS  Google Scholar 

  21. Kudo, N. et al. A single oral administration of theaflavins increases energy expenditure and the expression of metabolic genes. PLoS One 10, e0137809 (2015)

    Article  Google Scholar 

  22. Choi, J. H. & Yun, J. W. Chrysin induces brown fat-like phenotype and enhances lipid metabolism in 3T3-L1 adipocytes. Nutrition 32, 1002–1010 (2016)

    Article  CAS  Google Scholar 

  23. Suárez-Zamorano, N. et al. Microbiota depletion promotes browning of white adipose tissue and reduces obesity. Nat. Med. 21, 1497–1501 (2015)

    Article  Google Scholar 

  24. Carmody, R. N. et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015)

    Article  CAS  Google Scholar 

  25. Sonnenburg, E. D. et al. Diet-induced extinctions in the gut microbiota compound over generations. Nature 529, 212–215 (2016)

    Article  CAS  ADS  Google Scholar 

  26. Montani, J. P., Schutz, Y. & Dulloo, A. G. Dieting and weight cycling as risk factors for cardiometabolic diseases: who is really at risk? Obes. Rev.16 (Suppl 1), 7–18 (2015)

  27. Rakoff-Nahoum, S., Paglino, J., Eslami-Varzaneh, F., Edberg, S. & Medzhitov, R. Recognition of commensal microflora by toll-like receptors is required for intestinal homeostasis. Cell 118, 229–241 (2004)

    Article  CAS  Google Scholar 

  28. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010)

    Article  CAS  Google Scholar 

  29. Thaiss, C. A. et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell 159, 514–529 (2014)

    Article  CAS  Google Scholar 

  30. Levy, M. et al. Microbiota-modulated metabolites shape the intestinal microenvironment by regulating NLRP6 inflammasome signaling. Cell 163, 1428–1443 (2015)

    Article  CAS  Google Scholar 

  31. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014)

    Article  CAS  Google Scholar 

  32. Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000)

    Article  CAS  Google Scholar 

  33. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)

    Article  CAS  Google Scholar 

  34. Marco-Sola, S., Sammeth, M., Guigó, R. & Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 9, 1185–1188 (2012)

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank the members of the Elinav and Segal laboratories for discussions and C. Bar-Nathan for germ-free mouse work. C.A.T. received a Boehringer Ingelheim Fonds PhD Fellowship. D.R. received a Levi Eshkol PhD Scholarship for Personalized Medicine by the Israeli Ministry of Science. Y.K. is the incumbent of the Sarah and Rolando Uziel Research Associate Chair. E.S. is supported by the Crown Human Genome Center; the Else Kroener Fresenius Foundation; D. L. Schwarz; J. N. Halpern; L. Steinberg; and grants funded by the European Research Council, the National Institute of Health, and the Israel Science Foundation. E.E. is supported by Y. and R. Ungar; the Abisch Frenkel Foundation for the Promotion of Life Sciences; the Gurwin Family Fund for Scientific Research; the Leona M. and Harry B. Helmsley Charitable Trust; the Crown Endowment Fund for Immunological Research; the estate of J. Gitlitz; the estate of L. Hershkovich; the Benoziyo Endowment Fund for the Advancement of Science; the Adelis Foundation; J. L. and V. Schwartz; A. and G. Markovitz; A. and C. Adelson; the French National Center for Scientific Research (CNRS); D. L. Schwarz; the V. R. Schwartz Research Fellow Chair; L. Steinberg; J. N. Halpern; A. Edelheit; and by grants funded by the European Research Council; a Marie Curie Integration grant; the German-Israeli Foundation for Scientific Research and Development; the Israel Science Foundation; the Minerva Foundation; the Rising Tide Foundation; the Helmholtz Foundation; and the European Foundation for the Study of Diabetes. E.E. is the incumbent of the Rina Gudinski Career Development Chair and a senior fellow of the Canadian Institute for Advanced Research (CIFAR).

Author information

Authors and Affiliations

Authors

Contributions

C.A.T. conceived and led the project, designed and performed experiments, and wrote the manuscript. S.I. designed and performed experiments, and wrote the manuscript. D.R. developed and performed bioinformatics methods and analysis, and wrote the manuscript. M.M., M.L., C.M., L.D., S.B. and S.R. performed experiments. S.M., M.D.-B., Y.K. and I.B. performed flavonoid measurements, next-generation sequencing, metabolic measurements, and MRI, respectively. A.G. provided essential tools. A.H., H.S., Z.H. and A.A. provided insights and supervised parts of the experimental work. E.S. and E.E. conceived and directed the project, designed experiments, supervised the participants, and wrote the manuscript.

Corresponding authors

Correspondence to Eran Segal or Eran Elinav.

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The authors declare no competing financial interests.

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Nature thanks C. Nagler and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 Metabolic measurements during weight regain.

a, Schematic indicating time point of metabolic measurements. b, Quantification of weight regain by weight gain. c, Net weight gain induced by 8 weeks of HFD in weight-cycling mice and continuous HFD. d, e, Coronal (above) and axial (below) MRI scans (d), and quantification of body fat content (e). fh, Serum levels of leptin (f), LDL (g), and HDL (h) during the second HFD exposure of mice undergoing weight cycling and controls. i, j, Quantification of dark phase (i) and light phase (j) energy expenditure upon weight regain of weight-cycling mice. kq, Representative recordings (k, n, q) and quantifications (l, m, o, p) of O2 consumption (km), CO2 consumption (np), and food intake (q) upon weight regain of weight-cycling mice. Experiments were repeated twice. Shown are mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 by ANOVA (ep) or Mann–Whitney U-test (b, c). See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 2 Enhanced recurrent weight gain after initial obesity.

a, b, The effect of celastrol on weight loss in mice continuously fed a HFD (a) and mice with alternating diets (b). ce, Weight regain quantification by AUC (c), weight regain slope (d,), and net weight gain on HFD (e) by control mice and weight-cycling mice treated with celastrol to lose weight. f, g, Quantification of weight regain by AUC (f), weight regain slope (g) of leptin antagonist-treated weight-cycling mice and controls. h, i, Body fat content (h), and serum cholesterol levels (i) of weight-cycling mice undergoing a third weight cycle and controls. j, Schematic of the analysed time point in ko. k, Weight gain during 4 weeks of HFD. lo, Glucose levels after glucose tolerance test (l), glucose level quantification (m), serum cholesterol levels (n), and quantification of body fat content (o) in weight-cycling mice during initial obesity and controls. Experiments were repeated twice. Shown are mean ± s.e.m. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001 by ANOVA (c, f, h, i, m, n, o) or Mann–Whitney U-test (d, e, g). See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 3 Recovery of metabolic parameters after dieting.

a, Schematic of the analysed nadir time point. bf, Body fat content (b), serum cholesterol levels (c), glucose levels after glucose tolerance test (d), glucose level quantification (e), and serum insulin levels (f) in weight-cycling mice upon return to normal weight. gl, Representative recordings (g, j) and quantifications (h, i, k, l) of O2 and CO2 consumption by weight-cycling mice upon return to normal weight and controls. Experiments were repeated twice. Data are mean ± s.e.m. n.s., not significant by ANOVA. See Supplementary Table 5 for exact n values.

Extended Data Figure 4 Metabolic measurements after dieting.

Representative recordings (a, d, g, j), dark phase (b, e, h, k), and light phase (c, f, i, l) quantifications of energy expenditure (ac), physical activity (df), food intake (gi), and water consumption (jl) of weight-cycling mice during weight regain and controls. Experiments were repeated twice. Shown are mean ± s.e.m. n.s., not significant by ANOVA. See Supplementary Table 5 for exact n values.

Extended Data Figure 5 Persistent microbiome changes after dieting.

a, Quantification of UniFrac distances of weight-cycling mice from NC controls at the indicated time points. Plots correspond to the PCoA analyses in Fig. 2b–f. bd, PCoA analyses and distance quantification (insets) of V3/V4-targeted 16S sequencing of mice before (b), during (c), and after (d) diet-induced obesity and subsequent weight loss. ei, Examples of OTUs whose abundance does (e, f) or does not (gi) recover after dieting. jm, PCoA analyses (j, k), UniFrac distance (l) and alpha diversity (m) at the nadir time point of post-dieting mice that had received celastrol to accelerate weight loss. Experiments were repeated twice. Data are mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 6 Persistent metagenomic changes after dieting.

a, Heatmap of normalized gene abundance in the microbiota of mice before, during, and after obesity. b, c, Examples of genes whose abundance does not recover after dieting. d, PCA of bacterial KEGG modules over time in weight-cycling mice and controls. ej, Examples of KEGG pathways whose abundance is reversibly decreased (e, f), reversibly increased (g, h), or persistently decreased (i, j) during obesity and dieting. Data are from one experiment. Data are mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 7 The post-dieting microbiota drives enhanced recurrent obesity.

a, b, PCoA (a) and alpha diversity (b) of faecal microbiota after dieting (nadir time point at week 8) from mice with or without antibiotic treatment during weight loss. c, Net weight gain induced by 8 weeks of HFD in weight-cycling mice or continuous HFD control with or without antibiotic treatment between weeks 4 and 8. d, e, Glucose levels after oral glucose tolerance test (d) and glucose level quantification after glucose tolerance test (e) during weight regain in mice with or without antibiotic treatment during weight loss (n = 4–10). fh, PCoA of faecal microbiota from formerly obese mice and controls at the time of dieting-induced weight normalization (f), 15 weeks after weight normalization (g), and 21 weeks after weight normalization (h). ik, Correlation analysis (i) and examples (j, k) of microbial taxa undergoing gradual normalization in abundance over a period of 21 weeks after weight normalization. l, Quantification of secondary weight gain after microbiota normalization. Experiments were repeated twice. Data are mean ± s.e.m. **P < 0.01, ***P < 0.001 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 8 Transfer, prediction and treatment of weight regain by microbiome features.

a, Schematic of microbiota transfer to germ-free mice after dieting. Recipients were fed either a HFD or NC. bd, PCoA of recipient microbiota (b) and relative UniFrac distances to NC controls (c, d) of germ-free mice one week after transplantation with microbiota from weight-cycling mice or controls, and fed either NC (c) or a HFD (d). e, f, Quantifications of weight gain (e) and blood glucose after glucose tolerance test (f) in germ-free recipients of microbiota from weight cycling mice or controls. g, h, Correlation of predicted and measured weight gain when prediction is based solely on inferred history of obesity (g) or solely on 16S sequencing (h). ik, PCoA of faecal microbiota (i) and relative UniFrac distances between donors (j) and recipients (k) two weeks after the onset of daily FMT from cycHFD or NC mice to mice undergoing weight cycling. lo, Quantification of secondary weight gain (l), net weight gain induced by 8 weeks of HFD feeding (m), glucose levels after glucose tolerance test (n) and lean mass (o) in weight-cycling mice and controls with or without FMT. Experiments were repeated twice. Data are mean ± s.e.m. n.s., not significant. *P < 0.05, **P < 0.01, ****P < 0.0001 by ANOVA (e, f, l, m, o) or Mann–Whitney U-test (c, d, j, k). See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 9 Persistent metabolomic changes after dieting.

ah, Examples of metabolites whose abundance is reversibly decreased (a, b), reversibly increased (c, d), persistently decreased (e, f), or persistently increased (g, h) during obesity and dieting. i, Schematic of flavonoid biosynthetic pathways leading to the production and conversion of naringenin. KEGG IDs of key enzymes are indicated. Genes found in our metagenomic dataset are indicated in green. Data are from one experiment. Shown are mean ± s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 10 Microbiota control of post-dieting metabolic complications through intestinal flavonoids.

a, Schematic showing sampling times in obesity/recovery cycle experiment. b, Dietary flavonoids in NC and HFD. ce, Abundance of flavanone 4-reductase (c), chalcone synthase (d), and eriodictyol (e) over time in the faeces of mice undergoing weight cycling normalized to controls. f, Quantification of flavanone 4-reductase levels in faecal DNA relative to host DNA in weight-cycling mice at the end of the weight loss period, with or without antibiotic treatment during weight loss. g, Schematic of sampling time upon weight regain. h, i, Abundance of apigenin (h) and naringenin (i) in the faeces of mice undergoing post-dieting weight regain and controls. jm, Flavonoid levels (j), PCoA of faecal microbiota (k), net weight gain induced by 8 weeks of HFD (l), and weight regain quantification by AUC (m) of weight-cycling mice supplemented with apigenin and naringenin during the weight regain. Data are from one (ak) or two (l, m) experiments. Shown are mean ± s.e.m. *P < 0.05, **P < 0.01 by ANOVA (c, e, l) or Mann–Whitney U-test (h, m). See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 11 Metabolic measurements in flavonoid-treated mice.

Representative recordings (a, d, g, j), dark phase quantifications (b, e, h, k), and light phase quantifications (c, f, i, l) of O2 consumption (ac), CO2 consumption (df), respiratory exchange ratio (gi), and physical activity (jl) of weight-cycling mice with or without supplementation of apigenin and naringenin (A/N) during weight regain. Data are from one experiment. Data are mean ± s.e.m. **P < 0.01, ***P < 0.001 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 12 Metabolic measurements in flavonoid- and antibiotic-treated mice.

Representative recordings (a, d), dark phase quantifications (b, e), and light phase quantifications (c, f) of food (ac) and water consumption (df), of weight-cycling mice with or without supplementation of apigenin and naringenin (A/N) during weight regain. g, Schematic indicating time of metabolic measurements during the weight regain phase. h, i, Quantifications of energy expenditure in weight-cycling mice with or without antibiotic treatment during weight loss. jl, Representative recording (j) and quantifications (k, l) of O2 consumption by weight-cycling mice with or without antibiotic treatment (Abx) during weight loss. Data are from one experiment. Shown are mean ± s.e.m. *P < 0.05, ***P < 0.001 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

Extended Data Figure 13 Metabolic measurements in antibiotic-treated mice.

Representative recordings (a, d, g, f), dark phase quantifications (b, e, h, k), and light phase quantifications (c, f, i, l) of CO2 consumption (ac), respiratory exchange ratio (df), physical activity (gi) and food intake (jl) by weight-cycling mice with or without antibiotic treatment (Abx) during weight loss. Data are from one experiment. Data are mean ± s.e.m. ***P < 0.01 by ANOVA. See Supplementary Tables 5 and 6 for exact n and P values.

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Supplementary Table 5

This table contains the exact n values for each panel. (XLSX 13 kb)

Supplementary Table 6

This table contains the exact p-values for each panel. (XLSX 10 kb)

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Thaiss, C., Itav, S., Rothschild, D. et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540, 544–551 (2016). https://doi.org/10.1038/nature20796

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