Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Animal Models

A new animal diet based on human Western diet is a robust diet-induced obesity model: comparison to high-fat and cafeteria diets in term of metabolic and gut microbiota disruption

International Journal of Obesity volume 42, pages 525–534 (2018)Cite this article

Subjects

Abstract

Background/Objectives:

Obesity is a metabolic disorder that predisposes patients to numerous diseases and has become a major global public-health concern. Animal models of diet-induced obesity (DIO) are frequently used to study obesity, but which DIO model most accurately reflects the pathology of human obesity remains unclear. In this study, we designed a diet based on the human Western diet (WD) and compared it with the cafeteria diet (CAF) and high-fat diet (HFD) in order to evaluate which diet most closely mirrors human obesity.

Methods:

Wistar rats were fed four different diets (WD, CAF, HFD and a low-fat diet) for 18 weeks. Metabolic parameters and gut microbiota changes were then characterized.

Results:

Rats fed the four different diets exhibited completely different phenotypes, highlighting the importance of diet selection. This study also revealed that WD most effectively induced obesity and obesity-related disorders, and thus proved to be a robust model of human obesity. Moreover, WD-fed rats developed obesity and obesity-related comorbidities independent of major alterations in gut microbiota composition (dysbiosis), whereas CAF-fed rats developed the greatest dysbiosis independent of obesity. We also characterized gut microbiota after feeding on these four different diets and identified five genera that might be involved in the pathogenesis of obesity.

Conclusions:

These data suggest that diet, and not the obese state, was the major driving force behind gut microbiota changes. Moreover, the marked dysbiosis observed in CAF-fed rats might have resulted from the presence of several additives present in the CAF diet, or even a lack of essential vitamins and minerals. Based on our findings, we recommend the use of the prototypic WD (designed here) in DIO models. Conversely, CAF could be used to investigate the effects of excessive consumption of industrially produced and highly processed foods, which are characteristic of Western society.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Haslam DW, James WPT . Obesity. Lancet 2005; 366: 1197–1209.

    Article  Google Scholar 

  2. Smyth S, Heron A . Diabetes and obesity: the twin epidemics. Nat Med 2006; 12: 75–80.

    Article  CAS  Google Scholar 

  3. Cordain L, Eaton SB, Sebastian A, Mann N, Lindeberg S, Watkins BA et al. Origins and evolution of the Western diet: health implications for the 21st century. Am J Clin Nutr 2005; 81: 341–354.

    Article  CAS  Google Scholar 

  4. National Center for Health Statistics Health, United States, 2015: with special feature on racial and ethnic health disparities. National Center for Health Statistics: Hyattsville, MD, 2016.

  5. Buettner R, Schölmerich J, Bollheimer LC . High-fat diets: modeling the metabolic disorders of human obesity in rodents. Obesity 2007; 15: 798–808.

    Article  CAS  Google Scholar 

  6. Nilsson C, Raun K, Yan F, Larsen MO, Tang-Christensen M . Laboratory animals as surrogate models of human obesity. Acta Pharmacol Sin 2012; 33: 173–181.

    Article  CAS  Google Scholar 

  7. Sampey BP, Vanhoose AM, Winfield HM, Freemerman AJ, Muehlbauer MJ, Fueger PT et al. Cafeteria diet is a robust model of human metabolic syndrome with liver and adipose inflammation: comparison to high-fat diet. Obesity 2011; 19: 1109–1117.

    Article  CAS  Google Scholar 

  8. Higa TS, Spinola AV, Fonseca-Alaniz MH . Evangelista FSA. Comparison between cafeteria and high-fat diets in the induction of metabolic dysfunction in mice. Int J Physiol Pathophysiol Pharmacol 2014; 6: 47–54.

    PubMed  PubMed Central  Google Scholar 

  9. Moore BJ . The cafeteria diet-an inappropriate tool for studies of thermogenesis. J Nutr 1987; 117: 227–231.

    Article  CAS  Google Scholar 

  10. Shen J, Obin MS, Zhao L . The gut microbiota, obesity and insulin resistance. Mol Aspects Med 2013; 34: 39–58.

    Article  CAS  Google Scholar 

  11. Sonnenburg JL, Bäckhed F . Diet–microbiota interactions as moderators of human metabolism. Nature 2016; 535: 56–64.

    Article  CAS  Google Scholar 

  12. Lecomte V, Kaakoush NO, Maloney CA, Raipuria M, Huinao KD, Mitchell HM et al. Changes in gut microbiota in rats fed a high fat diet correlate with obesity-associated metabolic parameters. PLoS One 2015; 10: e0126931.

    Article  Google Scholar 

  13. Boulangé CL, Neves AL, Chilloux J, Nicholson JK, Dumas M-E . Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med 2016; 8: 42.

    Article  Google Scholar 

  14. National Research Council Guide for the Care and Use of Laboratory Animals. 8th edn. The National Academies Press: Washington, DC, USA, 2011.

  15. Reeves PG, Nielsen FH, Fahey GC . AIN-93 purified diets for laboratory rodents: final report of the American Institute of Nutrition ad hoc writing committee on the reformulation of the AIN-76A rodent diet. J Nutr 1993; 123: 1939–1951.

    Article  CAS  Google Scholar 

  16. Grotto D, Zied E . The standard American diet and its relationship to the health status of Americans. Nutr Clin Pract 2010; 25: 603–612.

    Article  Google Scholar 

  17. US Department of Agriculture. Nutrient intakes from food: mean amounts of consumed per individual, by gender and age. What we eat in America. Natl Heal Nutr Exam Surv 2011–2012 2012; 1–9.

  18. Berthoud HR, Zheng H . Modulation of taste responsiveness and food preference by obesity and weight loss. Physiol Behav 2012; 107: 527–532.

    Article  CAS  Google Scholar 

  19. Oliveros E, Somers VK, Sochor O, Goel K, Lopez-Jimenez F . The concept of normal weight obesity. Prog Cardiovasc Dis 2014; 56: 426–433.

    Article  Google Scholar 

  20. Thorburn AN, Macia L, Mackay CR . Diet, metabolites, and ‘western-lifestyle’ inflammatory diseases. Immunity 2014; 40: 833–842.

    Article  CAS  Google Scholar 

  21. de Macedo IC, de Freitas JS, da Silva Torres IL . The influence of palatable diets in reward system activation: a mini review. Adv Pharmacol Sci 2016; 2016: 1–7.

    Article  Google Scholar 

  22. Bellentani S, Scaglioni F, Marino M, Bedogni G . Epidemiology of non-alcoholic fatty liver disease. Dig Dis 2010; 28: 155–161.

    Article  Google Scholar 

  23. Adams La, Angulo P . Recent concepts in non-alcoholic fatty liver disease. Diabet Med 2005; 22: 1129–1133.

    Article  CAS  Google Scholar 

  24. Ouchi N, Parker JL, Lugus JJ, Walsh K . Adipokines in inflammation and metabolic disease. Nat Rev Immunol 2011; 11: 85–97.

    Article  CAS  Google Scholar 

  25. Gavrila A, Chan JL, Yiannakouris N, Kontogianni M, Miller LC, Orlova C et al. Serum adiponectin levels are inversely associated with overall and central fat distribution but are not directly regulated by acute fasting or leptin administration in humans: cross-sectional and interventional studies. J Clin Endocrinol Metab 2003; 88: 4823–4831.

    Article  CAS  Google Scholar 

  26. Ahmad S, Khan MS, Akhter F, Khan MS, Khan A, Ashraf JM et al. Glycoxidation of biological macromolecules: a critical approach to halt the menace of glycation. Glycobiology 2014; 24: 979–990.

    Article  CAS  Google Scholar 

  27. Teodoro JS, Varela AT, Rolo AP, Palmeira CM . High-fat and obesogenic diets: current and future strategies to fight obesity and diabetes. Genes Nutr 2014; 9: 406.

    Article  Google Scholar 

  28. Veteläinen R, van Vliet A, van Gulik TM . Essential pathogenic and metabolic differences in steatosis induced by choline or methione-choline deficient diets in a rat model. J Gastroenterol Hepatol 2007; 22: 1526–1533.

    Article  Google Scholar 

  29. Zeisel SH, da Costa K-A . Choline: an essential nutrient for public health. Nutr Rev 2009; 67: 615–623.

    Article  Google Scholar 

  30. Kucera O . Experimental models of non-alcoholic fatty liver disease in rats. World J Gastroenterol 2014; 20: 8364–8376.

    Article  Google Scholar 

  31. Turnbaugh PJ, Bäckhed F, Fulton L, Gordon JI . Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 2008; 3: 213–223.

    Article  CAS  Google Scholar 

  32. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G et al. Richness of human gut microbiome correlates with metabolic markers. Nature 2013; 500: 541–546.

    Article  CAS  Google Scholar 

  33. DeGruttola AK, Low D, Mizoguchi A, Mizoguchi E . Current understanding of dysbiosis in disease in human and animal models. Inflamm Bowel Dis 2016; 22: 1137–1150.

    Article  Google Scholar 

  34. Rosini TC, Ramos da Silva AS, de Moraes C . Diet-induced obesity: rodent model for the study of obesity-related disorders. Rev Assoc Med Bras 2012; 58: 383–387.

    PubMed  Google Scholar 

  35. Damms-Machado A, Weser G, Bischoff SC . Micronutrient deficiency in obese subjects undergoing low calorie diet. Nutr J 2012; 11: 34.

    Article  CAS  Google Scholar 

  36. Via M . The malnutrition of obesity: micronutrient deficiencies that promote diabetes. ISRN Endocrinol 2012; 2012: 1–8.

    Article  Google Scholar 

  37. Tremaroli V, Bäckhed F . Functional interactions between the gut microbiota and host metabolism. Nature 2012; 489: 242–249.

    Article  CAS  Google Scholar 

  38. Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y-Y, Keilbaugh SA et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 2011; 334: 105–108.

    Article  CAS  Google Scholar 

  39. Brown K, DeCoffe D, Molcan E, Gibson DL . Diet-induced dysbiosis of the intestinal microbiota and the effects on immunity and disease. Nutrients 2012; 4: 1095–1119.

    Article  CAS  Google Scholar 

  40. Rabot S, Membrez M, Blancher F, Berger B, Moine D, Krause L et al. High fat diet drives obesity regardless the composition of gut microbiota in mice. Sci Rep 2016; 6: 32484.

    Article  CAS  Google Scholar 

  41. Reardon S . A mouse’s house may ruin experiments. Nature 2016; 530: 264–264.

    Article  CAS  Google Scholar 

  42. Collins FS, Tabak LA . NIH plans to enhance reproducibility. Nature 2014; 505: 612–613.

    Article  Google Scholar 

  43. Casadevall A, Fang FC . Reproducible science. Infect Immun 2010; 78: 4972–4975.

    Article  CAS  Google Scholar 

  44. Servick K . Mouse microbes may make scientific studies harder to replicate. Science 2016 doi:10.1126/science.aah7199.

  45. Hariri N, Thibault L . High-fat diet-induced obesity in animal models. Nutr Res Rev 2010; 23: 270–299.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Funding for this project was provided by the Brazilian funding agencies CNPq (402471/2013-0), CAPES, FAPERGS (PRONEX 16/2551-0000 499-4) and PROPESQ-UFRGS.

Author information

Authors and Affiliations

  1. Centro de Estudos em Estresse Oxidativo—Departamento de Bioquímica – Instituto de Ciências Básicas da Saúde—Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brasil

    R C Bortolin, A R Vargas, J Gasparotto, P R Chaves, C E Schnorr, A K Silveira, D P Gelain & J C F Moreira

  2. Departamento de Ingeniería Civil y Ambiental—Universidad de la Costa, Barranquilla, Atlántico, Colombia

    Kd B Martinello

  3. Departamento de Educação em Saúde—Campus Universitário Professor Antônio Garcia Filho—Universidade Federal de Sergipe (UFS), Lagarto, SE, Brasil

    T K Rabelo

Authors
  1. R C Bortolin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A R Vargas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J Gasparotto
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P R Chaves
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C E Schnorr
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kd B Martinello
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. A K Silveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. T K Rabelo
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. D P Gelain
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. J C F Moreira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R C Bortolin.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bortolin, R., Vargas, A., Gasparotto, J. et al. A new animal diet based on human Western diet is a robust diet-induced obesity model: comparison to high-fat and cafeteria diets in term of metabolic and gut microbiota disruption. Int J Obes 42, 525–534 (2018). https://doi.org/10.1038/ijo.2017.225

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2017.225

This article is cited by

Search

Advanced search

Quick links