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.

  • Article
  • Published:

Ketogenic diet-induced bile acids protect against obesity through reduced calorie absorption

Nature Metabolism volume 6, pages 1397–1414 (2024)Cite this article

Subjects

Abstract

The low-carbohydrate ketogenic diet (KD) has long been practiced for weight loss, but the underlying mechanisms remain elusive. Gut microbiota and metabolites have been suggested to mediate the metabolic changes caused by KD consumption, although the particular gut microbes or metabolites involved are unclear. Here, we show that KD consumption enhances serum levels of taurodeoxycholic acid (TDCA) and tauroursodeoxycholic acid (TUDCA) in mice to decrease body weight and fasting glucose levels. Mechanistically, KD feeding decreases the abundance of a bile salt hydrolase (BSH)-coding gut bacterium, Lactobacillus murinus ASF361. The reduction of L. murinus ASF361 or inhibition of BSH activity increases the circulating levels of TDCA and TUDCA, thereby reducing energy absorption by inhibiting intestinal carbonic anhydrase 1 expression, which leads to weight loss. TDCA and TUDCA treatments have been found to protect against obesity and its complications in multiple mouse models. Additionally, the associations among the abovementioned bile acids, microbial BSH and metabolic traits were consistently observed both in an observational study of healthy human participants (n = 416) and in a low-carbohydrate KD interventional study of participants who were either overweight or with obesity (n = 25). In summary, we uncover a unique host–gut microbiota metabolic interaction mechanism for KD consumption to decrease body weight and fasting glucose levels. Our findings support TDCA and TUDCA as two promising drug candidates for obesity and its complications in addition to a KD.

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

Fig. 1: KD enhances serum TDCA and TUDCA levels to decrease body weight and fasting glucose levels.
Fig. 2: The gut microbiota is required for the KD-induced metabolic benefits and upregulation of TDCA and TUDCA.
Fig. 3: Three gut microbial strains correlate with TDCA, TUDCA and KD-driven metabolic phenotypes.
Fig. 4: L. murinus ASF361 regulates the deconjugation of TDCA and TUDCA through expressing BSH.
Fig. 5: TDCA and TUDCA attenuate intestinal lipid absorption by downregulating Car1 and protect mice against obesity.
Fig. 6: TDCA, TUDCA and microbial BSHs associate with BMI and fasting glucose in humans.

Similar content being viewed by others

Data availability

The raw data for this study are accessible through NODE (http://www.biosino.org/node, Project ID OEP005260). These include serum metabolomic data, metagenomic data, L. murinus ASF361 genomic data and intestinal transcriptome data from animal studies as well as basic characteristic data, BA-targeted metabolomic data and metagenomic data from human studies. Upon justified request and appropriate approvals, the data can be provided by submitting a formal application to the Oversight Group of the different cohorts, through their respective corresponding author. All shared data will be de-identified. For any additional information needed to reanalyze the reported data, please contact the lead authors: S. Hong (shangyu_hong@fudan.edu.cn) or Y. Zheng (yan_zheng@fudan.edu.cn). All other data supporting the findings of this study are provided as source data files accompanying this paper. Source data are provided with this paper.

References

  1. World Obesity Federation. World obesity atlas 2022 https://www.worldobesity.org/resources/resource-library/world-obesity-atlas-2022 (2022).

  2. Afshin, A. et al. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 377, 13–27 (2017).

    Article  PubMed  Google Scholar 

  3. Di Angelantonio, E. et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet (North Am. Ed.) 388, 776–786 (2016).

    Google Scholar 

  4. Powell-Wiley, T. M. et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation 143, E984–E1010 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kumanyika, S. & Dietz, W. H. Solving population-wide obesity—progress and future prospects. N. Engl. J. Med. 383, 2197–2200 (2020).

    Article  PubMed  Google Scholar 

  6. Zhu, H. et al. Ketogenic diet for human diseases: the underlying mechanisms and potential for clinical implementations. Signal Transduct. Target Ther. 7, 11 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Abbasi, J. Interest in the ketogenic diet grows for weight loss and type 2 diabetes. JAMA 319, 215–217 (2018).

    Article  PubMed  Google Scholar 

  8. Dashti, H. M. et al. Beneficial effects of ketogenic diet in obese diabetic subjects. Mol. Cell. Biochem. 302, 249–256 (2007).

    Article  CAS  PubMed  Google Scholar 

  9. Hall, K. D. et al. Energy expenditure and body composition changes after an isocaloric ketogenic diet in overweight and obese men. Am. J. Clin. Nutr. 104, 324–333 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kennedy, A. R. et al. A high-fat, ketogenic diet induces a unique metabolic state in mice. Am. J. Physiol. Endocrinol. Metab. 292, E1724–E1739 (2007).

    Article  CAS  PubMed  Google Scholar 

  11. Badman, M. K., Kennedy, A. R., Adams, A. C., Pissios, P. & Maratos-Flier, E. A very low carbohydrate ketogenic diet improves glucose tolerance in ob/ob mice independently of weight loss. Am. J. Physiol. Endocrinol. Metab. 297, E1197–E1204 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pissios, P. et al. Methionine and choline regulate the metabolic phenotype of a ketogenic diet. Mol. Metab. 2, 306–313 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Olson, C. A. et al. The gut microbiota mediates the anti-seizure effects of the ketogenic diet. Cell 173, 1728–172 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ang, Q. Y. et al. Ketogenic diets alter the gut microbiome resulting in decreased intestinal Th17 cells. Cell 181, 1263–126 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li, Y. et al. Ketogenic diets induced glucose intolerance and lipid accumulation in mice with alterations in gut microbiota and metabolites. mBio 12, e03601-20 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Mardinoglu, A. et al. An integrated understanding of the rapid metabolic benefits of a carbohydrate-restricted diet on hepatic steatosis in humans. Cell Metab. 27, 559–55 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Cai, J., Rimal, B., Jiang, C., Chiang, J. Y. L. & Patterson, A. D. Bile acid metabolism and signaling, the microbiota, and metabolic disease. Pharmacol. Ther. 237, 108238 (2022).

    Article  CAS  PubMed  Google Scholar 

  18. Cai, J., Sun, L. & Gonzalez, F. J. Gut microbiota-derived bile acids in intestinal immunity, inflammation, and tumorigenesis. Cell Host Microbe 30, 289–300 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Guzior, D. V. & Quinn, R. A. Review: microbial transformations of human bile acids. Microbiome 9, 140 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fuchs, R. T., Grundy, F. J. & Henkin, T. M. The SMK box is a new SAM-binding RNA for translational regulation of SAM synthetase. Nat. Struct. Mol. Biol. 13, 226–233 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Supuran, C. T. Anti-obesity carbonic anhydrase inhibitors: challenges and opportunities. J. Enzym. Inhib. Med. Chem. 37, 2478–2488 (2022).

    Article  CAS  Google Scholar 

  22. Konstantopoulos, N. et al. Methazolamide is a new hepatic insulin sensitizer that lowers blood glucose in vivo. Diabetes 61, 2146–2154 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Vullo, D., Nishimori, I., Innocenti, A., Scozzafava, A. & Supuran, C. T. Carbonic anhydrase activators: an activation study of the human mitochondrial isoforms VA and VB with amino acids and amines. Bioorg. Med. Chem. Lett. 17, 1336–1340 (2007).

    Article  CAS  PubMed  Google Scholar 

  24. Jiang, C. et al. Intestine-selective farnesoid X receptor inhibition improves obesity-related metabolic dysfunction. Nat. Commun. 6, 10166 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Gonzalez, F. J., Jiang, C. T. & Patterson, A. D. An intestinal microbiota-farnesoid X receptor axis modulates metabolic disease. Gastroenterology 151, 845–859 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Akwabi-Ameyaw, A. et al. Conformationally constrained farnesoid X receptor (FXR) agonists: naphthoic acid-based analogs of GW 4064. Bioorg. Med. Chem. Lett. 18, 4339–4343 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. Han, Y.-M., Ramprasath, T. & Zou, M.-H. β-Hydroxybutyrate and its metabolic effects on age-associated pathology. Exp. Mol. Med. 52, 548–555 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Liu, X. et al. Effects of a low-carbohydrate diet on weight loss and cardiometabolic profile in Chinese women: a randomised controlled feeding trial. Br. J. Nutr. 110, 1444–1453 (2013).

    Article  CAS  PubMed  Google Scholar 

  29. Foley, M. H. et al. Bile salt hydrolases shape the bile acid landscape and restrict Clostridioides difficile growth in the murine gut. Nat. Microbiol 8, 611–628 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ahlin, S. et al. Bile acid changes after metabolic surgery are linked to improvement in insulin sensitivity. Br. J. Surg. 106, 1178–117 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Chaudhari, S. N. et al. Bariatric surgery reveals a gut-restricted TGR5 agonist with anti-diabetic effects. Nat. Chem. Biol. 17, 20–29 (2021).

    Article  CAS  PubMed  Google Scholar 

  32. Zhu, B. et al. Reduced glycodeoxycholic acid levels are associated with negative clinical outcomes of gestational diabetes mellitus. J. Zhejiang Univ. Sci. B 22, 223–232 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Qi, X. et al. Gut microbiota-bile acid-interleukin-22 axis orchestrates polycystic ovary syndrome. Nat. Med. 25, 1225–122 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Quante, M. et al. Restored TDCA and valine levels imitate the effects of bariatric surgery. eLife 10, e62928 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chen, B. et al. Glycoursodeoxycholic acid regulates bile acids level and alters gut microbiota and glycolipid metabolism to attenuate diabetes. Gut Microbes 15, 2192155 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Huang, F. et al. Theabrownin from Pu-erh tea attenuates hypercholesterolemia via modulation of gut microbiota and bile acid metabolism. Nat. Commun. 10, 4971 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ding, L. et al. Notoginsenoside Ft1 acts as a TGR5 agonist but FXR antagonist to alleviate high fat diet-induced obesity and insulin resistance in mice. Acta Pharmaceutica Sin. B 11, 1541–1554 (2021).

    Article  CAS  Google Scholar 

  38. Li, H. T. et al. Resistant starch intake facilitates weight loss in humans by reshaping the gut microbiota. Nat. Metab. 6, 578–597 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, L. et al. Methionine restriction alleviates high-fat diet-induced obesity: involvement of diurnal metabolism of lipids and bile acids. Biochim. Biophys. Acta Mol. Basis Dis. 1866, 165908 (2020).

    Article  CAS  PubMed  Google Scholar 

  40. Suriano, F. et al. Novel insights into the genetically obese (ob/ob) and diabetic (db/db) mice: two sides of the same coin. Microbiome 9, 147 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Liu, C. et al. The Mouse Gut Microbial Biobank expands the coverage of cultured bacteria. Nat. Comm. 11, 79 (2020).

    Article  CAS  Google Scholar 

  42. Sayin, S. I. et al. Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist. Cell Metab. 17, 225–235 (2013).

    Article  CAS  PubMed  Google Scholar 

  43. Supuran, C. T. Carbonic anhydrase inhibitors as emerging drugs for the treatment of obesity. Expert Opin. Emerg. Drugs 17, 11–15 (2012).

    Article  CAS  PubMed  Google Scholar 

  44. Gadde, K. M. et al. Effects of low-dose, controlled-release, phentermine plus topiramate combination on weight and associated comorbidities in overweight and obese adults (CONQUER): a randomised, placebo-controlled, phase 3 trial. Lancet 377, 1341–1352 (2011).

    Article  CAS  PubMed  Google Scholar 

  45. Gadde, K. M., Franciscy, D. M., Wagner, H. R. 2nd & Krishnan, K. R. Zonisamide for weight loss in obese adults: a randomized controlled trial. JAMA 289, 1820–1825 (2003).

    Article  CAS  PubMed  Google Scholar 

  46. Caricilli, A. M. et al. Topiramate treatment improves hypothalamic insulin and leptin signaling and action and reduces obesity in mice. Endocrinology 153, 4401–4411 (2012).

    Article  CAS  PubMed  Google Scholar 

  47. Pastorekova, S., Parkkila, S., Pastorek, J. & Supuran, C. T. Carbonic anhydrases: current state of the art, therapeutic applications and future prospects. J. Enz. Inhibi. Med. Chem. 19, 199–291 (2024).

    Article  Google Scholar 

  48. Endeward, V., Kleinke, T. & Gros, G. Carbonic anhydrase in the gastrointestinal mucus of mammals—possible protective role against carbon dioxide. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 136, 281–287 (2003).

    Article  PubMed  Google Scholar 

  49. Gallo, V., De Micheli, A. G. & Chiandussi, L. Tauro-ursodeoxycholic acid vs. ursodeoxycholic acid in the dissolution of biliary calculi. Results of a single blind study. La Clin. Terapeutica 143, 421–428 (1993).

    CAS  Google Scholar 

  50. Wang, S. et al. Bile acid–microbiome interaction promotes gastric carcinogenesis. Adv. Sci. 9, 2200263 (2022).

    Article  CAS  Google Scholar 

  51. Sun, L. L. et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin. Nat. Med. 24, 1919–191 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cazares-Olivera, M. et al. Animal unit hygienic conditions influence mouse intestinal microbiota and contribute to T-cell-mediated colitis. Exp. Biol. Med (Maywood) 247, 1752–1763 (2022).

    Article  CAS  PubMed  Google Scholar 

  53. Adhikari, A. A. et al. Development of a covalent inhibitor of gut bacterial bile salt hydrolases. Nat. Chem. Biol. 16, 318–31 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wu, J. et al. Rhein modulates host purine metabolism in intestine through gut microbiota and ameliorates experimental colitis. Theranostics 10, 10665–10679 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Plovier, H. et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med. 23, 107–113 (2017).

    Article  CAS  PubMed  Google Scholar 

  56. Zhong, W. et al. High-protein diet prevents fat mass increase after dieting by counteracting Lactobacillus-enhanced lipid absorption. Nat. Metab. 4, 1713–1731 (2022).

    Article  CAS  PubMed  Google Scholar 

  57. Jiang, Z. et al. Dietary fruit and vegetable intake, gut microbiota, and type 2 diabetes: results from two large human cohort studies. BMC Med. 18, 371 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wang, Y. et al. Sample collection, DNA extraction, and library construction protocols of the human microbiome studies in the International Human Phenome Project. Phenomics 3, 300–308 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).

    Article  CAS  PubMed  Google Scholar 

  61. Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinf. 11, 119 (2010).

    Article  Google Scholar 

  62. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).

    Article  CAS  PubMed  Google Scholar 

  63. Song, Z. et al. Taxonomic profiling and populational patterns of bacterial bile salt hydrolase (BSH) genes based on worldwide human gut microbiome. Microbiome 7, 9 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

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

    Article  CAS  PubMed  Google Scholar 

  66. Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–36 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Ho, J., Tumkaya, T., Aryal, S., Choi, H. & Claridge-Chang, A. Moving beyond P values: data analysis with estimation graphics. Nat. Methods 16, 565–566 (2019).

    Article  CAS  PubMed  Google Scholar 

  69. Mina, A. I. et al. CaIR: a web-based analysis tool for indirect calorimetry experiments. Cell Metab. 28, 656–65 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

S.H. was supported by the National Key R&D Program of China (2019YFA0802302, 2018YFA0800600, 2021YFA0804801), National Natural Science Foundation of China (32271217, 31971082, 91957117), Shanghai Natural Science Foundation (23ZR1403700) and Open Research Fund of the National Key Laboratory of Genetic Engineering (SKLGE-2315). Y.Z. was supported by the National Key R&D Program of China (2021YFA1301000) and Shanghai Municipal Science and Technology Major Project (2023SHZDZX02). This work was also supported by the Strategic Priority CAS Project (XDB38010300), Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) and the National Natural Science Foundation of China (81970684). We thank M. Chee, assistant professor at the University of Alberta, P. Pissios, senior scientist at Janssen Inc., K. Liao, professor at Shanghai Institute of Nutrition and Health, and Q. Tang and S. Zhao, professors at Fudan University, for comments that greatly improved this work. The data analysis server is supported by the Human Phenome Data Center of Fudan University, and we thank the center staff for their support. The computations in this research were performed using the CFFF platform of Fudan University.

Author information

Author notes

  1. These authors contributed equally: Xiao Li, Jie Yang, Xiaofeng Zhou.

Authors and Affiliations

  1. State Key Laboratory of Genetic Engineering, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, P.R. China

    Xiao Li, Jie Yang, Xiaofeng Zhou, Chen Dai, Mengmeng Kong, Linshan Xie, Chenglin Liu, Yilian Liu, Dandan Li, Xiaonan Ma, Li Jin, Huiru Tang, Yan Zheng & Shangyu Hong

  2. Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai, P.R. China

    Chenglin Liu

  3. Department of Cardiology, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Disease, Shanghai, P.R. China

    Yuxiang Dai & Yan Zheng

  4. Masonic Medical Research Institute, Utica, NY, USA

    Yan Sun

  5. Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, P.R. China

    Zhijie Jian

  6. Institute for Immunology, Tsinghua University, Beijing, P.R. China

    Xiaohuan Guo

  7. Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, P.R. China

    Xu Lin

  8. Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, P.R. China

    Xu Lin

  9. Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, P.R. China

    Yixue Li

  10. Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, P.R. China

    Yixue Li

  11. School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, P.R. China

    Yixue Li

  12. Department of Nutrition and Food Hygiene, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, P.R. China

    Liang Sun

  13. Department of Epidemiology and Biostatistics, School of Public Health, Global Health Institute, Xi’an Jiaotong University Health Science Center, Xi’an, P.R. China

    Xin Liu

  14. Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Institute of Nutrition, Fudan University, Shanghai, P.R. China

    Yan Zheng

Authors
  1. Xiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaofeng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chen Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mengmeng Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Linshan Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chenglin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yilian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Dandan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiaonan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yuxiang Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Yan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhijie Jian
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Xiaohuan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Xu Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Yixue Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Liang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Li Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Huiru Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Shangyu Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X. Li and S.H. designed the animal studies. L.J., H.T. and Y.Z. designed the human observational study. X. Lin, Y.L., L.S. and X. Liu designed the LC feeding trial. X. Li, J.Y., C.D., L.X., Y.L., D.L., X.M., Y.D. and Z.J. conducted the experiments. X. Li, X.Z., M.K., C.L., Y.S., C.D., Y.L., D.L., X.M., Y.D., Y.Z. and S.H. analyzed the data. X. Li, J.Y., X.Z., Y.Z. and S.H. wrote the paper. X. Li was responsible for the visualization. Y.Z. and S.H. acquired the funding. X.G., L.J., H.T., Y.Z. and S.H. supervised the project.

Corresponding authors

Correspondence to Yan Zheng or Shangyu Hong.

Ethics declarations

Competing interests

A provisional patent application has been filed that covers aspects of this work. X. Li, J.Y., H.T., Y.Z. and S.H. are inventors on that patent application. The other authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Ara Koh 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.

Additional information

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

Extended data

Extended Data Fig. 1 KD affects the metabolism of specific taurine-conjugated BAs.

a, The curve of body weight change in mice (dietary intervention experiment one, six biological replicates for each group). Each fitted curve is calculated based on 23 days of measured body weight values over the course of the study. Shading represents the 95% CI; middle line represented the LOESS fitted curve. b, c, Body weight (b) and fasting glucose (c) after CD or KD consumption (six biological replicates for each group). d, Principal component analysis (PCA) of serum metabolomes (six biological replicates for each group). Each point represents one sample. e, f, Body weight (e) and fasting glucose (f) after CD, KD, or KDM consumption (dietary intervention experiment two, five biological replicates for each group). g, Percentage stratification of conjugated and unconjugated BAs in serum (five biological replicates for each group). h, Ratio of conjugated to unconjugated BAs in serum (five biological replicates for each group). i, Percentage stratification of primary and secondary BAs in serum (five biological replicates for each group). j, Ratio of primary to secondary BAs in serum (five biological replicates for each group). k, Serum free taurine levels (five biological replicates for each group). l, The levels of conjugated and unconjugated BAs in cecal contents (five biological replicates for each group). m, Boxplot showing the cecal content levels of six unconjugated BAs (five biological replicates for each group). Boxplot, median and quartiles; whiskers, data range. Error bars, s.e.m.; ns, not significant; **P < 0.01, ***P < 0.001 determined by two-tailed Student’s t-test (b, c), PERMANOVA based on the Euclid distance (d), and one-way ANOVA with Tukey’s post-hoc test (e-l). In g and i, %P < 0.05 (CD vs. KD); &P < 0.05 (KD vs. KDM).

Source data

Extended Data Fig. 2 Gut microbiota is critical for KD-induced improvement in glucose homeostasis.

a-d, GTT and the associated AUC values in ABX treatment experiment (five biological replicates for each group) (a, b) and FMT experiment one (ten biological replicates for each group) (c, d). e, f, Fasting glucose after FMT (e) and after stopping FMT (f) (FMT experiment two, five biological replicates for each group). g, h, Serum levels of THCA (g) and TαMCA/TβMCA (h) after dietary intervention and ABX treatment (five biological replicates for each group). Error bars, s.e.m.; ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test.

Source data

Extended Data Fig. 3 L. murinus ASF361 gavage affects body weight, fasting glucose and glucose tolerance.

a-d, Body weight (a), fasting glucose (b), GTT (c), and the associated AUC values (d) after 10 days of L. murinus and A. muciniphila administrations (KDM condition, bacterial strain administration experiment two, seven biological replicates for each group). e, f, Serum levels of THCA (e) and TβMCA (f) after L. murinus oral supplementation (KDM condition, bacterial strain administration experiment one, five biological replicates for each group). g, h, Cecal content levels of HCA (g) and βMCA (h) after L. murinus oral supplementation (bacterial strain administration experiment one, five biological replicates for each group). i, The genomic architecture of the L. murinus ASF361. From outside to center: forward strand coding sequence (CDS), reverse strand CDS, the scaffolds containing the genes described in the main text, GC content, GC skew value, genome size ruler. j, Growth curve of L. murinus with or without methionine in the MRS culture media fitted by 4-PL (n = five replicates/treatment). k-n, Changes in TDCA (k), TUDCA (l), DCA (m) and UDCA (n) levels in the MRS culture media with or without methionine after co-cultured with L. murinus ASF361 in vitro (three biological replicates for each group, Shadings indicate s.e.m.). o, The gene arrow map displaying the specific genetic elements in scaffold67 (GC content shown in the lower track) in the L. murinus genome. Error bars, s.e.m.; ns, not significant; **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test (a, b, and d), two-tailed Student’s t-test (e-h and k-n), and two-way ANOVA (j). Lactobacillus murinus ASF361 = L. murinus, Akkermansia muciniphila ATCC BAA-835 = A. muciniphila.

Source data

Extended Data Fig. 4 Interventions of methionine, microbes or BAs do not affect energy expenditure and intake.

a, Energy expenditure of vehicle-treated or TDCA-treated mice over 72 h and mean energy expenditure per mouse basis (corrected by body weight, five biological replicates for each group). b, Energy expenditure of vehicle-treated or TUDCA-treated mice over 72 h and mean energy expenditure per mouse basis (corrected by body weight, four biological replicates for each group). c, d, Daily food intake (c) (n = seven replicates/treatment) and water intake (d) (n = four replicates/treatment) per mouse basis in TDCA-treated and vehicle-treated groups. e, f, Daily food intake (e) (n = seven replicates/treatment) and water intake (f) (n = four replicates/treatment) per mouse basis in TUDCA-treated and vehicle-treated groups. g-i, Fecal energy in ABX treatment experiment (six biological replicates for each group) (g), FMT experiment one (five biological replicates for each group) (h), and bacteria strain administration experiment (seven biological replicates for each group) (i). Boxplot, median and quartiles; whiskers, data range. j-o, Daily food intake (n = seven replicates/intervention) and water intake (n = four replicates/ intervention) per mouse basis in ABX treatment experiment (j, k), FMT experiment one (l, m), and oral administration of bacterial strains experiment (n, o). No significant differences in all groups. Error bars, s.e.m.; ns, not significant ; *P < 0.05, **P < 0.01, ***P < 0.001 determined by two-tailed Student’s t-test (a-f) and one-way ANOVA with Tukey’s post-hoc test (g-o).

Source data

Extended Data Fig. 5 TDCA and TUDCA treatments significantly downregulate Car1 expression in vivo and in vitro.

a, RT-qPCR result of ileal mCar1 mRNA levels in mice after vehicle, TDCA or TUDCA treatment (normalized to the housekeeping gene, mHprt1) (six biological replicates for each group). b, RT-qPCR result of hCAR1 mRNA levels after the administration of PBS (control), TDCA, or TUDCA in Caco-2 cells (normalized to the housekeeping gene, hHPRT1) (four biological replicates for each group). c, Body weight of mice after vehicle or CAR inhibitors (MTZ and TPM) treatment (five biological replicates for each group). d, e, Representative images (d) and fluorescence quantification (e) of intestinal sections from mice gavaged with fatty acid conjugated BODIPY fluorophore after vehicle, MTZ, or TPM treatment (three biological replicates for each group). f, g, Representative images (f) and quantification (g) of Oil Red O staining of Caco-2 cells after vehicle, MTZ, or TPM treatment (three biological replicates for each group). h, Fecal energy of CD-fed mice after treatment with vehicle, TDCA, TUDCA, TDCA plus D-Phe or TUDCA plus D-Phe (five biological replicates for each group). Boxplot, median and quartiles; whiskers, data range. i-k, RT-qPCR results of ileal Shp (i), ileal Fgf15 (j) and liver Cyp7a1 (k) mRNA levels in mice after CD, KD, or KDM intervention (normalized to the housekeeping gene, Gapdh) (five biological replicates for each group). l, Serum total BA levels in mice after CD, KD or KDM intervention (five biological replicates for each group). m-o, RT-qPCR results of ileal Shp (m), ileal Fgf15 (n) and liver Cyp7a1 (o) mRNA levels in mice after vehicle or L. murinus gavage (normalized to the housekeeping gene, Gapdh) (five biological replicates for each group). p, Serum C4 (the primary BA precursor metabolite) levels in mice after vehicle or L. murinus gavage (five biological replicates for each group). q-s, Fecal energy (q), body weight (r) and fasting glucose (s) after CD, KD, or KD plus GW4064 intervention (five biological replicates for each group). Boxplot, median and quartiles; whiskers, data range. Error bars, s.e.m; ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test (a-l and q-s) and two-tailed Student’s t-test (m-p). Lactobacillus murinus ASF361 = L. murinus.

Source data

Extended Data Fig. 6 Interventions of methionine, microbes or BAs do not affect serum levels of ketone bodies.

a-d, Fasting ketones after ABX treatment (a, five biological replicates for each group), FMT (b, five biological replicates for each group), bacterial strains gavage (c, seven biological replicates for each group) or TDCA and TUDCA treatments (d, five biological replicates for each group). ns, not significant; ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test. Lactobacillus murinus ASF361 = L. murinus, Akkermansia muciniphila ATCC BAA-835 = A. muciniphila.

Source data

Extended Data Fig. 7 TDCA and TUDCA improve glucose tolerance and reduce hepatic lipid accumulation in obese and diabetic models.

a-d, GTT and the associated AUC values of DIO mice (a), STZ mice (b), ob/ob mice (c), db/db mice (d) after 3 weeks of TDCA and TUDCA treatments (five biological replicates for each group). e, f, Representative H&E-stained (e) and Oil Red O-stained (f) histological sections from livers of four mouse models (three biological replicates for each group). g-j, The hepatic triglycerides levels in DIO mice (g), STZ mice (h), ob/ob mice (i), db/db mice (j) after TDCA and TUDCA treatments (five biological replicates for each group). k-l, The hepatic triglycerides levels in CD-fed (k) and KDM-fed (l) mice after TDCA and TUDCA treatments (five biological replicates for each group). Error bars, s.e.m.; ns. not significant; *P < 0.05, **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test.

Source data

Extended Data Fig. 8 DCA and UDCA species associate with BMI and fasting glucose in humans.

a, b, Body weight (a) and fasting glucose (b) of CD-fed mice after 3 weeks of GDCA and GUDCA treatments (six biological replicates for each group). c, Heatmap displaying the correlations between plasma taurine-conjugated or glycine-conjugated DCA and UDCA levels (n = 406) or fecal unconjugated DCA and UDCA levels (n = 381) and BMI or fasting glucose. The dark blue and light blue rectangles represent the conjugated BAs and unconjugated BAs, respectively. d-g, The raincloud plots showing the differences in plasma levels of TDCA (d), GDCA (e), TUDCA (f) and GUDCA (g) in overweight individuals (BMI ≥ 24 kg m-2, n = 124) and non-overweight individuals (BMI < 24 kg m-2, n = 282). Boxplot, median and quartiles; whiskers, data range. h, i, The bar charts showing the differences in fecal levels of DCA (h) and UDCA (i) in overweight individuals (BMI ≥ 24 kg m-2, n = 123) and non-overweight individuals (BMI < 24 kg m-2, n = 258). Boxplot, median and quartiles; whiskers, data range. j, The raincloud plot showing the differences in microbial ‘obesity-related’ BSH gene abundance in overweight individuals (BMI ≥ 24 kg m-2, n = 115) and non-overweight individuals (BMI < 24 kg m-2, n = 264). Boxplot, median and quartiles; whiskers, data range. Error bars, s.e.m.; indicate SEM. #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001 determined by one-way ANOVA with Tukey’s post-hoc test (a, b), Spearman’s rank correlation (c), and two-sided Mann-Whitney U-test (d-j).

Source data

Extended Data Fig. 9 Changes in DCA and UDCA species and microbial BSHs associate with changes in BMI and fasting glucose in LC intervention participants.

a, Heatmap displaying the correlations between changes in plasma taurine-conjugated or glycine-conjugated DCA and UDCA levels, or fecal unconjugated DCA and UDCA levels and changes in BMI or fasting glucose levels induced by the 12-week intervention with LC. The dark blue and light blue rectangles represent the conjugated BAs and unconjugated BAs, respectively. b, Heatmap showing the correlations between changes in ‘obesity-related’ BSH gene abundance and changes in BMI or fasting glucose levels induced by 12-week intervention with LC. c, Heatmap displaying the correlations between changes in plasma taurine-conjugated or glycine-conjugated DCA and UDCA levels, or fecal unconjugated DCA and UDCA levels and changes in ‘obesity-related’ BSH gene abundance induced by the 12-week intervention with LC. The dark blue and light blue rectangles represent the conjugated BAs and unconjugated BAs, respectively. d, The circus correlation plot showing associations among plasma or fecal DCA and UDCA species levels, ‘obesity-related’ BSH gene abundance, BMI and fasting glucose levels. Red and blue represent positive and negative correlations, respectively. The black border highlights the significant associations with P < 0.05. The outer circle represents different omics. n = 24. #P < 0.1, *P < 0.05, **P < 0.01, ***P < 0.001 determined by Spearman’s rank correlation.

Source data

Extended Data Fig. 10 Summary diagram illustrating the metabolic regulatory mechanism of microbial BSH-dependent TDCA and TUDCA in KD consumption.

Created with BioRender.

Supplementary information

Supplementary Information

Supplementary Tables 1–11, Supplementary Tables 13–19, Supplementary Schematic Diagram, Resources.

Reporting Summary

Supplementary Table 12

The associations between the putative taxonomic BSH genes and BMI or fasting glucose.

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

Statistical source data.

Source Data Fig. 5i

Unprocessed western blots.

Source Data Fig. 6

Statistical source data.

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

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Yang, J., Zhou, X. et al. Ketogenic diet-induced bile acids protect against obesity through reduced calorie absorption. Nat Metab 6, 1397–1414 (2024). https://doi.org/10.1038/s42255-024-01072-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-024-01072-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing