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:

Total parenteral nutrition impairs glucose metabolism by modifying the gut microbiome

Nature Metabolism volume 5, pages 331–348 (2023)Cite this article

Subjects

Abstract

Total parenteral nutrition (TPN) can lead to complications, such as glucose metabolism disorders. While TPN is associated with impairments in intestinal function, the gut barrier and mucosal immunity, the relationship between the gut microbiome and TPN-related glucose metabolism disorders remains to be explored. In a cohort of 256 participants with type 2 intestinal failure, we show that parenteral nutrition providing >80% of total energy induces insulin resistance and a higher risk of complications. Using various male mouse models, we demonstrate that changes in Lactobacillaceae and indole-3-acetic acid (IAA) levels underlie these complications. Lactobacillaceae and IAA levels decrease in TPN-treated mice and participants, while their abundances in the latter are negatively correlated with insulin resistance and serum lipopolysaccharide levels. Furthermore, IAA activates the aryl hydrocarbon receptor and increases glucagon-like peptide-1 secretion through upregulation of Gcg expression and increased stem cell differentiation towards L cells. Finally, liraglutide, a glucagon-like peptide-1 receptor agonist, completely prevents TPN-induced glucose metabolism disorders in mice. Thus, TPN induces glucose metabolism disorders by altering the gut microbiota and its metabolites.

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: A higher level of calorie provision by parenteral nutrition is associated with glucose metabolism disorders and clinical complications in participants with type 2 intestinal failure.
Fig. 2: Total parenteral nutrition impairs insulin sensitivity, liver glycogen deposition, insulin-dependent signalling and hepatic function in mice.
Fig. 3: Total parenteral nutrition alters the composition of the gut microbiota.
Fig. 4: Total parenteral nutrition promotes poor insulin sensitivity by reducing the levels of tryptophan metabolites, including indole-3-acetic acid.
Fig. 5: AhR inhibitor CH223191 can induced the glucose metabolism disorders which cannot be reversed by indole-3-acetic acid.
Fig. 6: Inactivation of indole/aryl hydrocarbon receptor signalling contributes to parenteral nutrition-related impairment of insulin sensitivity.
Fig. 7: Inactivation of indole/AhR signalling inhibits GLP-1 production and AhR ligands can influence the ability of L cells to secrete GLP-1 through multiple pathways.
Fig. 8: The effect of liraglutide administration on glucose metabolism disorders associated with total parenteral nutrition.

Similar content being viewed by others

Data availability

All 16S RNA-sequencing data that support the findings of this research have been deposited in the Sequence Read Archive under accession code PRJNA915810. Source data are provided with this paper. Metabolomic data for mice and clinical data for participants are shown in Supplementary Data 1. Other datasets are available from the corresponding authors on reasonable request.

Code availability

Code used in the present study is shown in the Reporting Summary.

References

  1. Pironi, L. Definitions of intestinal failure and the short bowel syndrome. Best. Pract. Res. Clin. Gastroenterol. 30, 173–185 (2016).

    Article  PubMed  Google Scholar 

  2. Gardiner, K. R. Management of acute intestinal failure. Proc. Nutr. Soc. 70, 321–328 (2011).

    Article  PubMed  Google Scholar 

  3. Allan, P. & Lal, S. Intestinal failure: a review. F1000Res 7, 85 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Lappas, B. M., Patel, D., Kumpf, V., Adams, D. W. & Seidner, D. L. Parenteral nutrition: indications, access and complications. Gastroenterol. Clin. North Am. 47, 39–59 (2018).

    Article  PubMed  Google Scholar 

  5. Brown, S. K. et al. Intestinal failure: the evolving demographic and patient outcomes on home parenteral nutrition. Acta Paediatr. 107, 2207–2211 (2018).

    Article  PubMed  Google Scholar 

  6. Naberhuis, J. K., Deutsch, A. S. & Tappenden, K. A. Teduglutide-stimulated intestinal adaptation is complemented and synergistically enhanced by partial enteral nutrition in a neonatal piglet model of short bowel syndrome. JPEN J. Parenter. Enter. Nutr. 41, 853–865 (2017).

    Article  CAS  Google Scholar 

  7. Barnes, J. L., Hartmann, B., Holst, J. J. & Tappenden, K. A. Intestinal adaptation is stimulated by partial enteral nutrition supplemented with the prebiotic short-chain fructooligosaccharide in a neonatal intestinal failure piglet model. JPEN J. Parenter. Enter. Nutr. 36, 524–537 (2012).

    Article  CAS  Google Scholar 

  8. Wan, X. et al. Partial enteral nutrition preserves elements of gut barrier function, including innate immunity, intestinal alkaline phosphatase (IAP) level, and intestinal microbiota in mice. Nutrients 7, 6294–6312 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sun, H. et al. Partial enteral nutrition increases intestinal sIgA levels in mice undergoing parenteral nutrition in a dose-dependent manner. Int. J. Surg. 49, 74–79 (2018).

    Article  PubMed  Google Scholar 

  10. Dibb, M., Teubner, A., Theis, V., Shaffer, J. & Lal, S. Review article: the management of long-term parenteral nutrition. Aliment. Pharmacol. Ther. 37, 587–603 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Btaiche, I. F. & Khalidi, N. Metabolic complications of parenteral nutrition in adults, part 1. Am. J. Health Syst. Pharm. 61, 1938–1949 (2004).

    Article  CAS  PubMed  Google Scholar 

  12. Gosmanov, A. R. & Umpierrez, G. E. Management of hyperglycemia during enteral and parenteral nutrition therapy. Curr. Diab. Rep. 13, 155–162 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Cheung, N. W., Napier, B., Zaccaria, C. & Fletcher, J. P. Hyperglycemia is associated with adverse outcomes in patients receiving total parenteral nutrition. Diabetes Care 28, 2367–2371 (2005).

    Article  PubMed  Google Scholar 

  14. Kinnare, K. F., Bacon, C. A., Chen, Y., Sowa, D. C. & Peterson, S. J. Risk factors for predicting hypoglycemia in patients receiving concomitant parenteral nutrition and insulin therapy. J. Acad. Nutr. Diet. 113, 263–268 (2013).

    Article  PubMed  Google Scholar 

  15. Coudenys, E., Waele, E. D., Meers, G., Collier, H. & Pen, J. J. Inadequate glycemic control in patients receiving parenteral nutrition lowers survival: a retrospective observational trial. Clin. Nutr. Exp. 17, 1–7 (2018).

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  18. Vrieze, A. et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology 143, 913–916 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. de Groot, P. et al. Donor metabolic characteristics drive effects of faecal microbiota transplantation on recipient insulin sensitivity, energy expenditure and intestinal transit time. Gut 69, 502–512 (2020).

    Article  PubMed  Google Scholar 

  20. Wang, J. et al. Gut microbiota as a modulator of paneth cells during parenteral nutrition in mice. JPEN J. Parenter. Enter. Nutr. 42, 1280–1287 (2018).

    Article  CAS  Google Scholar 

  21. Blacher, E., Levy, M., Tatirovsky, E. & Elinav, E. Microbiome-modulated metabolites at the interface of host immunity. J. Immunol. 198, 572–580 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Roager, H. M. & Licht, T. R. Microbial tryptophan catabolites in health and disease. Nat. Commun. 9, 3294 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zelante, T. et al. Tryptophan catabolites from microbiota engage aryl hydrocarbon receptor and balance mucosal reactivity via interleukin-22. Immunity 39, 372–385 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Cervantes-Barragan, L. et al. Lactobacillus reuteri induces gut intraepithelial CD4+CD8αα+ T cells. Science 357, 806–810 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Natividad, J. M. et al. Impaired aryl hydrocarbon receptor ligand production by the gut microbiota is a key factor in metabolic syndrome. Cell Metab. 28, 737–749 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Lin, Y. H. et al. Aryl hydrocarbon receptor agonist indigo protects against obesity-related insulin resistance through modulation of intestinal and metabolic tissue immunity. Int. J. Obes. 43, 2407–2421 (2019).

    Article  Google Scholar 

  27. Gribble, F. M. & Reimann, F. Function and mechanisms of enteroendocrine cells and gut hormones in metabolism. Nat. Rev. Endocrinol. 15, 226–237 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Pironi, L. et al. ESPEN guidelines on chronic intestinal failure in adults. Clin. Nutr. 35, 247–307 (2016).

    Article  PubMed  Google Scholar 

  29. Rosmarin, D. K., Wardlaw, G. M. & Mirtallo, J. Hyperglycemia associated with high, continuous infusion rates of total parenteral nutrition dextrose. Nutr. Clin. Pract. 11, 151–156 (1996).

    Article  CAS  PubMed  Google Scholar 

  30. Ukleja, A. & Romano, M. M. Complications of parenteral nutrition. Gastroenterol. Clin. North Am. 36, 23–46 (2007).

    Article  PubMed  Google Scholar 

  31. Bodoky, G., Meguid, M. M., Yang, Z. J. & Laviano, A. Effects of different types of isocaloric parenteral nutrients on food intake and metabolic concomitants. Physiol. Behav. 58, 75–79 (1995).

    Article  CAS  PubMed  Google Scholar 

  32. Meguid, M. M. et al. Effects of continuous graded total parenteral nutrition on feeding indexes and metabolic concomitants in rats. Am. J. Physiol. 260, E126–E140 (1991).

    CAS  PubMed  Google Scholar 

  33. Canfora, E. E., Meex, R. C. R., Venema, K. & Blaak, E. E. Gut microbial metabolites in obesity, NAFLD and T2DM. Nat. Rev. Endocrinol. 15, 261–273 (2019).

    Article  CAS  PubMed  Google Scholar 

  34. Heneghan, A. F. et al. Parenteral nutrition decreases paneth cell function and intestinal bactericidal activity while increasing susceptibility to bacterial enteroinvasion. JPEN J. Parenter. Enter. Nutr. 38, 817–824 (2014).

    Article  CAS  Google Scholar 

  35. Liu, Y. et al. Gut microbiome fermentation determines the efficacy of exercise for diabetes prevention. Cell Metab. 31, 77–91 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Martin, A. M. et al. The gut microbiome regulates host glucose homeostasis via peripheral serotonin. Proc. Natl Acad. Sci. USA 116, 19802–19804 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716–724 (2018).

    Article  CAS  PubMed  Google Scholar 

  38. Lu, P. et al. Activation of aryl hydrocarbon receptor dissociates fatty liver from insulin resistance by inducing fibroblast growth factor 21. Hepatology 61, 1908–1919 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Roh, E. et al. Serum aryl hydrocarbon receptor ligand activity is associated with insulin resistance and resulting type 2 diabetes. Acta Diabetol. 52, 489–495 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Xu, C. X. et al. Aryl hydrocarbon receptor deficiency protects mice from diet-induced adiposity and metabolic disorders through increased energy expenditure. Int. J. Obes. 39, 1300–1309 (2015).

    Article  CAS  Google Scholar 

  41. Remillard, R. B. & Bunce, N. J. Linking dioxins to diabetes: epidemiology and biologic plausibility. Environ. Health Perspect. 110, 853–858 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Natividad, J. M. et al. Impaired aryl hydrocarbon receptor ligand production by the gut microbiota is a key factor in metabolic syndrome. Cell Metab. 28, 737–749 (2018).

    Article  CAS  PubMed  Google Scholar 

  43. Baggio, L. L. & Drucker, D. J. Biology of incretins: GLP-1 and GIP. Gastroenterology 132, 2131–2157 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. DeFronzo, R. A. et al. The effect of insulin on the disposal of intravenous glucose. Results from indirect calorimetry and hepatic and femoral venous catheterization. Diabetes 30, 1000–1007 (1981).

    Article  CAS  PubMed  Google Scholar 

  45. Ying, W. et al. Adipose tissue B2 cells promote insulin resistance through leukotriene LTB4/LTB4R1 signaling. J. Clin. Invest. 127, 1019–1030 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kalafateli, M. et al. Malnutrition and sarcopenia predict post-liver transplantation outcomes independently of the model for end-stage liver disease score. J. Cachexia Sarcopenia Muscle 8, 113–121 (2017).

    Article  PubMed  Google Scholar 

  47. Pichler, J., Chomtho, S., Fewtrell, M., Macdonald, S. & Hill, S. Body composition in paediatric intestinal failure patients receiving long-term parenteral nutrition. Arch. Dis. Child. 99, 147–153 (2014).

    Article  PubMed  Google Scholar 

  48. Cani, P. D. et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56, 1761–1772 (2007).

    Article  CAS  PubMed  Google Scholar 

  49. Lamas, B. et al. CARD9 impacts colitis by altering gut microbiota metabolism of tryptophan into aryl hydrocarbon receptor ligands. Nat. Med. 22, 598–605 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chimerel, C. et al. Bacterial metabolite indole modulates incretin secretion from intestinal enteroendocrine L cells. Cell Rep. 9, 1202–1208 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Sandoval, D. A. & D’Alessio, D. A. Physiology of proglucagon peptides: role of glucagon and GLP-1 in health and disease. Physiol. Rev. 95, 513–548 (2015).

    Article  CAS  PubMed  Google Scholar 

  52. Deng, G. et al. Glucagon-like peptide-2 modulates enteric Paneth cells immune response and alleviates gut inflammation during intravenous fluid infusion in mice with a central catheter. Front Nutr. 8, 688715 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Matthews, D. R. et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412–419 (1985).

    Article  CAS  PubMed  Google Scholar 

  54. Horan, T. C., Andrus, M. & Dudeck, M. A. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am. J. Infect. Control 36, 309–332 (2008).

    Article  PubMed  Google Scholar 

  55. Abt, M. C. et al. Commensal bacteria calibrate the activation threshold of innate antiviral immunity. Immunity 37, 158–170 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Folkes, L. K. & Wardman, P. Enhancing the efficacy of photodynamic cancer therapy by radicals from plant auxin (indole-3-acetic acid). Cancer Res. 63, 776–779 (2003).

    CAS  PubMed  Google Scholar 

  57. Ji, Y., Gao, Y., Chen, H., Yin, Y. & Zhang, W. Indole-3-acetic acid alleviates nonalcoholic fatty liver disease in mice via attenuation of hepatic lipogenesis and oxidative and inflammatory stress. Nutrients 11, 2062 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Guo, H. et al. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science 370, eaay9097 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 25, 402–408 (2001).

    Article  CAS  PubMed  Google Scholar 

  60. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Qi, Z. et al. BMP restricts stemness of intestinal Lgr5+ stem cells by directly suppressing their signature genes. Nat. Commun. 8, 13824 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Virtue, S. & Vidal-Puig, A. GTTs and ITTs in mice: simple tests, complex answers. Nat. Metab. 3, 883–886 (2021).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank all the participants who donated specimens for this study. This project was supported by the National Natural Science Foundation of China (81470797 to X.W., 81770531 to X.W., 82170575 to X.W. and 81700518 to L.Z.), the Science Foundation of Outstanding Youth in Jiangsu Province (BK20170009 to X.W.), the National Science and Technology Research Funding for Public Welfare Medical Projects (201502022 to X.W.) and ‘The 13th Five-Year Plan’ Foundation of Jiangsu Province for Medical Key Talents (ZDRCA2016091 to X.W.).

Author information

Author notes

  1. These authors contributed equally: Peng Wang, Haifeng Sun, Gulisudumu Maitiabula.

Authors and Affiliations

  1. Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China

    Peng Wang, Haifeng Sun, Gulisudumu Maitiabula, Li Zhang, Jianbo Yang, Yupeng Zhang, Xuejin Gao, Jieshou Li & Xinying Wang

  2. MOE Key Laboratory of Model Animal for Disease Study, Model Animal Research Center of the Medical School of Nanjing University, Nanjing, China

    Bin Xue

  3. Core Laboratory, Sir Run Run Hospital, Nanjing Medical University, Nanjing, China

    Bin Xue

  4. State Key Laboratory of Reproductive Medicine and China International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China

    Chao-Jun Li

Authors
  1. Peng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haifeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gulisudumu Maitiabula
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianbo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yupeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xuejin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jieshou Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bin Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chao-Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xinying Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.W. and H.S. take responsibility for the content of the manuscript, including the data and analysis; study concept and design: X.W., P.W. and H.S.; data acquisition: J.Y., G.M., L.Z., X.G. and Y.Z.; drafting of the manuscript: P.W., H.S., B.X., C.-J.L. and X.W.; animal experiments (during revised manuscript): G.M., P.W.; critical revision of the manuscript: all authors; study supervision: J.L. and X.W. The order of the first three co-authors was assigned following the contribution to this article. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Bin Xue, Chao-Jun Li or Xinying Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Metabolism thanks Ruiwen Heng 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 Flow-chart summarizing the enrollment of the study participants.

A total of 313 patients with type 2 IF were admitted to the Clinical Nutrition Center of Jinling Hospital, Nanjing, Jiangsu, China between August 2013 and August 2018. 49 patients were removed according to the exclusion criteria, and 256 patients participated in this study. 162 patients were located in the L-PN group, and 94 patients were located in the H-PN group. There were 69 patients willing to provide blood samples (44 in the L-PN group and 25 in the H-PN group).

Extended Data Fig. 2 Poor insulin sensitivity in mice administered TPN.

n = 8 animals over 3 independent experiments. (a) Body weight changes in TPN-treated/chow-fed mice (P = 0.0229 for 3rd day, P = 0.0010 for 4th day, P = 0.0307 for 5th day, P = 0.0045 for 6th day, P = 0.0447 for 7th day). b–c. Area under the curve values for IPGTTs (b), and for IPITTs (c) in Chow and TPN groups. (d) Body composition of different groups of mice. (e) Venn diagram showing the potential central role of insulin resistance in glucose dysmetabolism, lean mass loss, and clinical complications during parenteral nutrition. Data are shown as mean ± S.D. (a), median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (b-d). P-Values are determined by Mann-Whitney test (a-d). All statistical tests are two-sided.

Source data

Extended Data Fig. 3 Microbiome in Chow, TPN and Abx mouse model.

n = 5 animals over 3 independent experiments. (a) Microbiota load in Chow and TPN groups. (b) Heatmap showing the top 10 phyla in mice from the TPN and Chow groups. (c) Heatmap showing the top 10 genera in mice from the TPN and Chow groups. (d) Depth of reads obtained from each sample. (e) Observed number of species in different groups of mice (P = 1.1E-05 for Abx vs Chow). (f) Analysis of principal components of microbiome of mice located in five groups (Abx group, Chow group, TPN group, Chow→Abx group and TPN → Abx group). (g) Bacterial discriminant analysis based on the linear discriminant analysis (LDA) score between Chow→Abx group and TPN → Abx group. (h) The relative abundance of Lactobacillaceae in different groups of mice. Data are shown as median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (a, e, h). P-Values are determined by Mann-Whitney test (a), Kruskal-Wallis test with correction by Dunnett’s t test (e, h), Linear discriminant analysis Effect Size (g). All statistical tests are two-sided.

Source data

Extended Data Fig. 4 Cecal contents transplantation can induce glucose metabolism disorders and microbiome of patients with IF.

(a) Body weight changes in mice following cecal contents transplantation (P = 0.0283 at 4th week). b-c. Area under the curve values for IPGTTs (b, P = 1.5E-05 for Chow→Abx vs TPN → Abx), and for IPITTs (c). (d) Body composition of different groups of mice. (n = 5 animals over 3 independent experiments for a-d). (e), Microbiota load in 7 H-PN and 9 L-PN patients. (f), The observed OTU numbers in 7 H-PN and 9 L-PN patients. (g), Analysis of principal components of microbiome of 7 H-PN and 9 L-PN patients. (h), Bacterial discriminant analysis based on the linear discriminant analysis (LDA) score between 7 H-PN and 9 L-PN patients. (i), The abundances of Rhodocyclaceae, Ruminococcaceae, and Sphingomonadaceae in patients. (n = 9 in L-PN group; n = 7 in H-PN group for e-i). Data are shown as mean ± S.D. (a), median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (b-f, i). P-Values are determined by Kruskal-Wallis test with correction by Dunnett’s t test (b, c), Mann-Whitney test (a, d-f, i), Adosin based Bray-Curtis (g), Linear discriminant analysis Effect Size (h). All statistical tests are two-sided.

Source data

Extended Data Fig. 5 Changes in levels of indole derivatives may underlie the association between reduced Lactobacillus abundance and glucose dysmetabolism.

n = 5 animals over 3 independent experiments. (a) Heatmap showing the top 15 differential metabolites. (b) The top 10 most enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to the differential metabolites. (c) Body weight changes during IAA treatment in mice (P-value were 0.0459 at 3rd day, 0.0439 at 9th day, 0.0150 at 11th day, 0.0088 at 12th day, 0.0088 at 13th day, 0.0088 at 14th day). (d) The blood concentration of IAA at 1st, 4th, 8th, 12th hour after intraperitoneal injection of IAA in mice (P- value were 0.0239, 4.6E-07, 0.0039 from left to right). (e) The blood concentration of IAA at the samples harvest time point. f-g. Area under the curve values for IPGTTs (f), and IPITTs (g). (h) Body composition of different groups of mice. Data are shown as mean ± S.D. (c, d), median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (e-h). P-Values are determined by GO enrichment analysis (b), Mann-Whitney test (c, d, h), Kruskal-Wallis test with correction by Dunnett’s t test (e-g). All statistical tests are two-sided.

Source data

Extended Data Fig. 6 Effects of kynurenic acid treatment on glucose metabolism disorders associated with TPN.

(a) Schematic of kynurenic acid-related experiments. (b) The blood concentration of kynurenic acid at the samples harvest time point. c–d. IPGTTs for the two groups of mice (IPGTTs: P = 0.0361 for 60 mins, P = 0.0283 for 120 mins). (c); area under the curve values for IPGTTs (d). (IPGTTs:). e–f. IPITTs (e); values of area under the curve for IPITTs (f). g–h. Representative histologic images showing deposition of glycogen in mouse hepatocytes (g) and quantification of the average levels of liver glycogen (h). (i) Western blot (left) and semiquantitative analyses (right) of insulin-driven glycogen synthesis signaling (IRS1-Akt-GSK3β) in the liver. (j) Body weight changes of mice. (k) Body composition of different groups of mice. (n = 5 animals over 3 independent experiments for b-k). (l) The serum concentration of kynurenic acid in patients with intestinal failure (n = 69). Data are shown as mean ± SEM (c, e), median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (b, d, f, h, i, k, l). P-Values are determined by Kruskal-Wallis test with correction by Dunnett’s t test (b), Mann-Whitney test (c-f, h-l). All statistical tests are two-sided.

Source data

Extended Data Fig. 7 Effects of AhR activation and inactivation on glucose tolerance and insulin sensitivity.

(a) Body weight changes of mice during the intraperitoneal treatment with CH223191 and IAA (* for Chow+ DMSO vs. Chow+CH223191; # for Chow+ DMSO vs Chow+CH22319 + IAA, P-Value for * were 0.0927, 0.0278, 0.0278, 0.0163, 0.0117, 0.0086, 0.0088, 0.0088, 0.0090, 0.0090, 0.0090 from left to right; P-value for # were 0.0283, 0.0090, 0.0090. 0.0086, 0.0090, 0.0088, 0.0088, 0.0088, 0.0090, 0.0088, 0.0090 from left to right). b–c. Area under the curve values for intraperitoneal glucose tolerance tests (b) and intraperitoneal insulin tolerance tests (c), showing the effects of an inhibitor of AhR (CH223191). (d) Body composition of different groups of mice (P-Values were 3.8E-06, 2.1E-06 from left to right). (n = 5 animals over 3 independent experiments for a-d) (e) Body weight changes of mice during intraperitoneal treatment with CH223191 and FICZ. f–g. Area under the curve values for intraperitoneal glucose tolerance tests (f, P-Values were 5.1E-15, 1.6E-06, 1.3E-08 from top to bottom) and intraperitoneal insulin tolerance tests (g), showing the effects of an agonist of AhR (CH223191). (h) Body composition of different groups of mice (P-Values were 3.1E-14, 1.0E-12, 6.0E-06 between groups from left to right; 7.9E-06 for TPN + DMSO vs TPN + FICZ in fat mass). (n = 8 animals over 3 independent experiments for e-h). Data are shown as mean ± S.D. (a, e), median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (b-d, f-h). P-Values are determined by Mann-Whitney test (a, e), Kruskal-Wallis test with correction by Dunnett’s t test (b-d, f-h). All statistical tests are two-sided.

Source data

Extended Data Fig. 8 AhR ligand can influence the ability of L cells to secrete GLP-1 through multiple pathways.

(a) Expression of Gcg mRNA in the terminal ileum and colon. (b) Western blot analysis of GLP-1 expression in the terminal ileum and colon. (n = 8 animals over 3 independent experiments). (c) Level of expression of genes associated with GLP-1 secretion and switch towards L-Cells phenotype in human organoids cultured with IAA for 5 days (n = 3 samples over 2 independent experiments, * means P < 0.05. P-values were 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0495, 0.0463, 0.0495, 0.0495 from left to right). Data are shown as median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (a, b), mean ± S.D. (c). P-Values are determined by Kruskal-Wallis test with correction by Dunnett’s t test (a), Mann-Whitney test (b), Students’ t test (c). All statistical tests are two-sided.

Source data

Extended Data Fig. 9 The effect of liraglutide administration on glucose metabolism disorders associated with TPN.

n = 7 animals over 3 independent experiments. (a) Body weight changes of mice during intraperitoneal treatment with liraglutide (* for Chow+ Saline group vs. TPN + Saline group; # for Chow+ Saline group vs TPN + Liraglutide group. P-Values for * were 0.0058, 0.0058, 0.0087, 0.0213, 0.0175, 0.0288 from left to right; P-Values for # were 0.0348, 0.0032, 0.0027, 0.0049, 0.0032, 0.0026 from left to right). b–c. Area under the curve values for intraperitoneal glucose tolerance tests (b) and intraperitoneal insulin tolerance tests (c). (d) Body composition of different groups of mice (P-Values were 4.8E-09, 1.6E-09 between groups from left to right; 7.2E-05 for fat mass in TPN + Saline vs TPN + Liraglutide; 5.3E-05 for lean body mass in TPN + Saline vs TPN + Liraglutide). (e) Serum alanine transaminase (ALT) and aspartate transaminase (AST) concentrations. (f) Liver edema evaluated from the liver/body weight ratio. (g) Serum lipopolysaccharide (LPS) concentration. Data are shown as mean ± S.D. (a), median (solid line) with 1st (lower dotted line) and 3rd (upper dotted line) quartiles in violin plot (b-g). P-Values are determined by Mann-Whitney test (a), Kruskal-Wallis test with correction by Dunnett’s t test (b-g). All statistical tests are two-sided.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1–7

Reporting Summary

Supplementary Data 1

Source data for metabolomics, 256 patients and 69 patients (sheets 1, 2 and 3, respectively).

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 2

Unprocessed western blots and PAS staining.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 3

Unprocessed western blots and PAS staining.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

Unprocessed western blots and PAS staining.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed western blots and PAS staining.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed western blots and PAS staining.

Source Data Fig. 7

Statistical source data.

Source Data Fig. 7

Immunohistochemistry of GLP-1/PYY. Mouse and human intestinal organoids.

Source Data Fig. 8

Statistical source data.

Source Data Fig. 8

Unprocessed western blots and PAS staining.

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. 6

Unprocessed western blots and PAS staining.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 8

Unprocessed 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

Wang, P., Sun, H., Maitiabula, G. et al. Total parenteral nutrition impairs glucose metabolism by modifying the gut microbiome. Nat Metab 5, 331–348 (2023). https://doi.org/10.1038/s42255-023-00744-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42255-023-00744-8

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