Non-caloric artificial sweeteners (NAS) are among the most widely used food additives worldwide, regularly consumed by lean and obese individuals alike. NAS consumption is considered safe and beneficial owing to their low caloric content, yet supporting scientific data remain sparse and controversial. Here we demonstrate that consumption of commonly used NAS formulations drives the development of glucose intolerance through induction of compositional and functional alterations to the intestinal microbiota. These NAS-mediated deleterious metabolic effects are abrogated by antibiotic treatment, and are fully transferrable to germ-free mice upon faecal transplantation of microbiota configurations from NAS-consuming mice, or of microbiota anaerobically incubated in the presence of NAS. We identify NAS-altered microbial metabolic pathways that are linked to host susceptibility to metabolic disease, and demonstrate similar NAS-induced dysbiosis and glucose intolerance in healthy human subjects. Collectively, our results link NAS consumption, dysbiosis and metabolic abnormalities, thereby calling for a reassessment of massive NAS usage.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


Primary accessions

European Nucleotide Archive

Data deposits

Sequencing data are deposited in the European Nucleotide Archive accession PRJEB6996.


  1. 1.

    et al. Nonnutritive sweeteners: current use and health perspectives. Diabetes Care 35, 1798–1808 (2012)

  2. 2.

    & Position of the Academy of Nutrition and Dietetics: use of nutritive and nonnutritive sweeteners. Journal of the Academy of Nutrition and Dietetics 112, 739–758 (2012)

  3. 3.

    & Effect of drinking soda sweetened with aspartame or high-fructose corn syrup on food intake and body weight. Am. J. Clin. Nutr. 51, 963–969 (1990)

  4. 4.

    , & Response to single dose of aspartame or saccharin by NIDDM patients. Diabetes Care 11, 230–234 (1988)

  5. 5.

    et al. Diet soda intake and risk of incident metabolic syndrome and type 2 diabetes in the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care 32, 688–694 (2009)

  6. 6.

    , , & Sucralose metabolism and pharmacokinetics in man. Food Chem. Toxicol. 38 (Suppl. 2). 31–41 (2000)

  7. 7.

    & The metabolism of saccharin in laboratory animals. Food Cosmet. Toxicol. 11, 391–402 (1973)

  8. 8.

    , , & The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012)

  9. 9.

    et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012)

  10. 10.

    et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011)

  11. 11.

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

  12. 12.

    , , & Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)

  13. 13.

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

  14. 14.

    et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179–185 (2012)

  15. 15.

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

  16. 16.

    et al. The NIH human microbiome project. Genome Res. 19, 2317–2323 (2009)

  17. 17.

    , & How glycan metabolism shapes the human gut microbiota. Nature Rev. Microbiol. 10, 323–335 (2012)

  18. 18.

    et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 18, 190–195 (2010)

  19. 19.

    Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990)

  20. 20.

    et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013)

  21. 21.

    , , , & Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes. Mol. Biosyst. 6, 909–921 (2010)

  22. 22.

    et al. Changes in gut microbiota control metabolic endotoxemia-induced inflammation in high-fat diet-induced obesity and diabetes in mice. Diabetes 57, 1470–1481 (2008)

  23. 23.

    et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013)

  24. 24.

    et al. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 307, 1955–1959 (2005)

  25. 25.

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

  26. 26.

    et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013)

  27. 27.

    , & Toxicological studies with sodium cyclamate and saccharin. Food Cosmet. Toxicol. 6, 313–327 (1968)

  28. 28.

    Acute and subchronic toxicity of sucralose. Food Chem. Toxicol. 38 (Suppl. 2). 53–69 (2000)

  29. 29.

    et al. Aspartame: a safety evaluation based on current use levels, regulations, and toxicological and epidemiological studies. Crit. Rev. Toxicol. 37, 629–727 (2007)

  30. 30.

    et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010)

  31. 31.

    et al. NLRP6 inflammasome regulates colonic microbial ecology and risk for colitis. Cell 145, 745–757 (2011)

  32. 32.

    et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)

  33. 33.

    et al. High-throughput chromatin immunoprecipitation for genome-wide mapping of in vivo protein–DNA interactions and epigenomic states. Nature Protocols 8, 539–554 (2013)

  34. 34.

    et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)

  35. 35.

    , , & The GEM mapper: fast, accurate and versatile alignment by filtration. Nature Methods 9, 1185–1188 (2012)

  36. 36.

    et al. Pathoscope: species identification and strain attribution with unassembled sequencing data. Genome Res. 23, 1721–1729 (2013)

  37. 37.

    et al. d-amino acids trigger biofilm disassembly. Science 328, 627–629 (2010)

  38. 38.

    , , & Development of a food frequency questionnaire (FFQ) for an elderly population based on a population survey. J. Nutr. 133, 3625–3629 (2003)

  39. 39.

    , , , & Development of a semi-quantitative Food Frequency Questionnaire (FFQ) to assess dietary intake of multiethnic populations. Eur. J. Epidemiol. 18, 855–861 (2003)

  40. 40.

    et al. Dietary evaluation and attenuation of relative risk: multiple comparisons between blood and urinary biomarkers, food frequency, and 24-hour recall questionnaires: the DEARR study. J. Nutr. 135, 573–579 (2005)

Download references


We thank the members of the Elinav and Segal laboratories for discussions. We acknowledge C. Bar-Nathan for germ-free mouse caretaking. We thank the Weizmann Institute management and the Nancy and Stephen Grand Israel National Center for Personalized Medicine (INCPM) for providing financial and infrastructure support. We thank G. Malka, N. Kosower and R. Bikovsky for coordinating the human clinical trials, and M. Pevsner-Fischer, T. Avnit-Sagi and M. Lotan-Pompan for assistance with microbiome sample processing. C.A.T. is the recipient of a Boehringer Ingelheim Fonds PhD Fellowship. G.Z.-S. is supported by the Morris Kahn Fellowships for Systems Biology. This work was supported by grants from the National Institute of Health (NIH) and the European Research Council (ERC) to E.S., and support and grants to E.E. provided by Y. and R. Ungar, the Abisch Frenkel Foundation for the Promotion of Life Sciences, the Gurwin Family Fund for Scientific Research, Leona M. and Harry B. Helmsley Charitable Trust, Crown Endowment Fund for Immunological Research, estate of J. Gitlitz, estate of L. Hershkovich, Rising Tide foundation, Minerva Stiftung foundation, and the European Research Council. E.E. is the incumbent of the Rina Gudinski Career Development Chair.

Author information

Author notes

    • Tal Korem
    • , David Zeevi
    •  & Gili Zilberman-Schapira

    These authors contributed equally to this work.


  1. Department of Immunology, Weizmann Institute of Science, Rehovot 76100, Israel

    • Jotham Suez
    • , Gili Zilberman-Schapira
    • , Christoph A. Thaiss
    • , Ori Maza
    • , Hagit Shapiro
    •  & Eran Elinav
  2. Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel

    • Tal Korem
    • , David Zeevi
    • , Adina Weinberger
    •  & Eran Segal
  3. Day Care Unit and the Laboratory of Imaging and Brain Stimulation, Kfar Shaul hospital, Jerusalem Center for Mental Health, Jerusalem 91060, Israel

    • David Israeli
  4. Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel

    • Niv Zmora
  5. Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel

    • Niv Zmora
    •  & Zamir Halpern
  6. Digestive Center, Tel Aviv Sourasky Medical Center, Tel Aviv 64239, Israel

    • Niv Zmora
    •  & Zamir Halpern
  7. The Nancy and Stephen Grand Israel National Center for Personalized Medicine (INCPM), Weizmann Institute of Science, Rehovot 76100, Israel

    • Shlomit Gilad
  8. Department of Veterinary Resources, Weizmann Institute of Science, Rehovot 76100, Israel

    • Yael Kuperman
    •  & Alon Harmelin
  9. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel

    • Ilana Kolodkin-Gal


  1. Search for Jotham Suez in:

  2. Search for Tal Korem in:

  3. Search for David Zeevi in:

  4. Search for Gili Zilberman-Schapira in:

  5. Search for Christoph A. Thaiss in:

  6. Search for Ori Maza in:

  7. Search for David Israeli in:

  8. Search for Niv Zmora in:

  9. Search for Shlomit Gilad in:

  10. Search for Adina Weinberger in:

  11. Search for Yael Kuperman in:

  12. Search for Alon Harmelin in:

  13. Search for Ilana Kolodkin-Gal in:

  14. Search for Hagit Shapiro in:

  15. Search for Zamir Halpern in:

  16. Search for Eran Segal in:

  17. Search for Eran Elinav in:


J.S. conceived the project, designed and performed experiments, interpreted the results, and wrote the manuscript. T.K., D.Z. and G.Z.-S. performed the computational and metagenomic microbiota analysis and the analysis of the retrospective and prospective human study, and are listed alphabetically. C.A.T., O.M., A.W. and H.S. helped with experiments. Y.K. helped with the metabolic cage experiments. S.G. designed the metagenomic library protocols and generated the libraries. I.K.-G. performed the SCFA quantification experiments. D.I., N.Z., and Z.H. performed and supervised human experimentation. A.H. supervised the germ-free mouse experiments. E.S. and E.E. conceived and directed the project, designed experiments, interpreted the results, and wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Eran Segal or Eran Elinav.

Extended data

Supplementary information

Excel files

  1. 1.

    Supplementary Information

    This file contains Supplementary Tables 1-7.

About this article

Publication history






Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.