Abstract
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.
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References
Gardner, C. et al. Nonnutritive sweeteners: current use and health perspectives. Diabetes Care 35, 1798–1808 (2012)
Fitch, C. & Keim, K. S. 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)
Tordoff, M. G. & Alleva, A. M. 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)
Horwitz, D. L., McLane, M. & Kobe, P. Response to single dose of aspartame or saccharin by NIDDM patients. Diabetes Care 11, 230–234 (1988)
Nettleton, J. A. 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)
Roberts, A., Renwick, A. G., Sims, J. & Snodin, D. J. Sucralose metabolism and pharmacokinetics in man. Food Chem. Toxicol. 38 (Suppl. 2). 31–41 (2000)
Byard, J. L. & Goldberg, L. The metabolism of saccharin in laboratory animals. Food Cosmet. Toxicol. 11, 391–402 (1973)
Clemente, J. C., Ursell, L. K., Parfrey, L. W. & Knight, R. The impact of the gut microbiota on human health: an integrative view. Cell 148, 1258–1270 (2012)
Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012)
Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970–974 (2011)
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)
Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: human gut microbes associated with obesity. Nature 444, 1022–1023 (2006)
Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012)
Henao-Mejia, J. et al. Inflammasome-mediated dysbiosis regulates progression of NAFLD and obesity. Nature 482, 179–185 (2012)
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014)
Peterson, J. et al. The NIH human microbiome project. Genome Res. 19, 2317–2323 (2009)
Koropatkin, N. M., Cameron, E. A. & Martens, E. C. How glycan metabolism shapes the human gut microbiota. Nature Rev. Microbiol. 10, 323–335 (2012)
Schwiertz, A. et al. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 18, 190–195 (2010)
Bergman, E. N. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev. 70, 567–590 (1990)
Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013)
Connor, S. C., Hansen, M. K., Corner, A., Smith, R. F. & Ryan, T. E. Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes. Mol. Biosyst. 6, 909–921 (2010)
Cani, P. D. 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)
Markle, J. G. et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013)
Sonnenburg, J. L. et al. Glycan foraging in vivo by an intestine-adapted bacterial symbiont. Science 307, 1955–1959 (2005)
Cani, P. D. et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56, 1761–1772 (2007)
Smith, M. I. et al. Gut microbiomes of Malawian twin pairs discordant for kwashiorkor. Science 339, 548–554 (2013)
Taylor, J. D., Richards, R. K. & Wiegand, R. G. Toxicological studies with sodium cyclamate and saccharin. Food Cosmet. Toxicol. 6, 313–327 (1968)
Goldsmith, L. A. Acute and subchronic toxicity of sucralose. Food Chem. Toxicol. 38 (Suppl. 2). 53–69 (2000)
Magnuson, B. A. et al. Aspartame: a safety evaluation based on current use levels, regulations, and toxicological and epidemiological studies. Crit. Rev. Toxicol. 37, 629–727 (2007)
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010)
Elinav, E. et al. NLRP6 inflammasome regulates colonic microbial ecology and risk for colitis. Cell 145, 745–757 (2011)
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006)
Blecher-Gonen, R. 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)
Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)
Marco-Sola, S., Sammeth, M., Guigó, R. & Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nature Methods 9, 1185–1188 (2012)
Francis, O. E. et al. Pathoscope: species identification and strain attribution with unassembled sequencing data. Genome Res. 23, 1721–1729 (2013)
Kolodkin-Gal, I. et al. d-amino acids trigger biofilm disassembly. Science 328, 627–629 (2010)
Shahar, D., Fraser, D., Shai, I. & Vardi, H. Development of a food frequency questionnaire (FFQ) for an elderly population based on a population survey. J. Nutr. 133, 3625–3629 (2003)
Shahar, D., Shai, I., Vardi, H., Brener-Azrad, A. & Fraser, D. Development of a semi-quantitative Food Frequency Questionnaire (FFQ) to assess dietary intake of multiethnic populations. Eur. J. Epidemiol. 18, 855–861 (2003)
Shai, I. 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)
Acknowledgements
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.
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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.
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Extended data figures and tables
Extended Data Figure 1 Experimental scheme.
10-week-old C57Bl/6 male mice were treated with the following dietary regimes. a, Drinking commercially available non-caloric artificial sweeteners (NAS; saccharin, sucralose and aspartame) or glucose, sucrose or water as controls and fed a normal-chow (NC) diet. b, Drinking commercially available saccharin or glucose as control and fed a high-fat diet (HFD). c, Drinking pure saccharin or water and fed HFD. d, As in c, but with outbred Swiss-Webster mice. Glucose tolerance tests, microbiome analysis and supplementation of drinking water with antibiotics were performed on the indicated time points. e, Schematic of faecal transplant experiments.
Extended Data Figure 2 Artificial sweeteners induce glucose intolerance.
a, AUC of mice fed HFD and commercial saccharin (N = 10) or glucose (N = 9). b, AUC of HFD-fed mice drinking 0.1 mg ml−1 saccharin or water for 5 weeks (N = 20), followed by ‘antibiotics A’ (N = 10). c, d, OGTT and AUC of HFD-fed outbred Swiss-Webster mice (N = 5) drinking pure saccharin or water. e, f, Faecal samples were transferred from donor mice (N = 10) drinking commercially available, pure saccharin, glucose or water controls into 8-week-old male Swiss-Webster germ-free recipient mice. AUC of germ-free mice 6 days following transplant of microbiota from commercial saccharin- (N = 12) and glucose-fed mice (N = 11) (e); or pure saccharin- (N = 16) and water-fed (N = 16) donors (f). Symbols (GTT) or horizontal lines (AUC), means; error bars, s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, ANOVA and Tukey post hoc analysis (GTT) or unpaired two-sided Student t-test (AUC). Each experiment was repeated twice.
Extended Data Figure 3 Metabolic characterization of mice consuming commercial NAS formulations.
10-week-old C57Bl/6 mice (N = 4) were given commercially available artificial sweeteners (saccharin, sucralose and aspartame) or controls (water, sucrose or glucose, N = 4 in each group) and fed normal-chow diet. After 11 weeks, metabolic parameters were characterized using the PhenoMaster metabolic cages system for 80 h. Light and dark phases are denoted by white and black rectangles on the x-axis, respectively, and grey bars for the dark phase. a, Liquids intake. b, AUC of a. c, Chow consumption. d, AUC of c. e, Total caloric intake from chow and liquid during 72 h (see methods for calculation). f, Respiratory exchange rate (RER). g, AUC of f. h, Physical activity as distance. i, AUC of h. j, Energy expenditure. k, Mass change compared to original mouse weight during 15 weeks (N = 10). l, AUC of k. The metabolic cages characterization and weight-gain monitoring were repeated twice.
Extended Data Figure 4 Metabolic characterization of mice consuming HFD and pure saccharin or water.
10-week-old C57Bl/6 mice (N = 8) were fed HFD, with or without supplementing drinking water with 0.1 mg ml−1 pure saccharin. After 5 weeks, metabolic parameters were characterized using the PhenoMaster metabolic cages system for 70 h. Light and dark phases are denoted by white and black rectangles on the x-axis, respectively, and grey bars for the dark phase. a, Liquids intake. b, AUC of a. c, Chow consumption. d, AUC of c. e, Respiratory exchange rate (RER). f, AUC of e. g, Physical activity as distance. h, AUC of g. i, Energy expenditure. The metabolic cages characterization was repeated twice.
Extended Data Figure 5 Glucose intolerant NAS-drinking mice display normal insulin levels and tolerance.
a, Fasting plasma insulin measured after 11 weeks of commercial NAS or controls (N = 10). b, Same as a, but measured after 5 weeks of HFD and pure saccharin or water (N = 20). c, Insulin tolerance test performed after 12 weeks of commercial NAS or controls (N = 10). Horizontal lines (a, b) or symbols (c) represent means; error bars, s.e.m. All measurements were performed on two independent cohorts.
Extended Data Figure 6 Dysbiosis in saccharin-consuming mice and germ-free recipients.
Heat map representing W11 logarithmic-scale fold taxonomic differences between commercial saccharin and water or caloric sweetener consumers (N = 5 in each group). Right column, taxonomical differences in germ-free mice following faecal transplantation from commercial saccharin- (recipients N = 15) or glucose-consuming mice (N = 13). OTU number (GreenGenes) and the lowest taxonomic level identified are denoted.
Extended Data Figure 7 Functional analysis of saccharin-modulated microbiota.
a, b, Changes in bacterial relative abundance occur throughout the bacterial genome. Shown are changes in sequencing coverage along 10,000 bp genomic regions of Bacteroides vulgatus (a) and Akkermansia muciniphila (b), with bins ordered by abundance in week 0 of saccharin-treated mice. c, Fold change in relative abundance of modules belonging to phosphotransferase systems (PTS) between week 11 and week 0 in mice drinking commercial saccharin, glucose or water. Module diagram source: KEGG database. d, Enriched KEGG pathways (fold change > 1.38 as cutoff) in mice consuming HFD and pure saccharin versus water compared to the fold change in relative abundance of the same pathways in mice consuming commercial saccharin (week 11/week 0).
Extended Data Figure 8 Saccharin directly modulates the microbiota.
a, Experimental schematic. b, Relative taxonomic abundance of anaerobically cultured microbiota. c, AUC of germ-free mice 6 days following transplantation with saccharin-enriched or control faecal cultures (N = 10 and N = 9, respectively). Horizontal lines, means; error bars, s.e.m. **P < 0.01, unpaired two-sided Student t-test. The experiment was repeated twice.
Extended Data Figure 9 Impaired glycaemic control associated with acute saccharin consumption in humans is transferable to germ-free mice.
a, Experimental schematic (N = 7). b, c, Daily incremental AUC of days 1–4 versus days 5–7 in four responders (b) or three non-responders (c). d, Principal coordinates analysis (PCoA) of weighted UniFrac distances of 16S rRNA sequences demonstrating separation on principal coordinates 2 (PC2), 3 (PC3) and 4 (PC4) of microbiota from responders (N samples = 12) versus non-responders (N = 8) during days 5–7. e, Order-level relative abundance of taxa samples from days 1–7 of responders and non-responders. f, AUC in germ-free mice (N = 6) 6 days following faecal transplantation from samples of responder 1 (R1) collected before and after 7 days of saccharin consumption. g, h, OGTT and AUC in germ-free mice (N = 5) 6 days after receiving faecal samples collected from responder 4 (R4) before and after 7 days of saccharin consumption. i, AUC in germ-free mice (N = 5) 6 days following faecal transplantation from samples of non-responder 3 (NR3) collected before and after 7 days of saccharin consumption. j, k, OGTT and AUC in germ-free mice (N = 5) 6 days after receiving faecal samples collected from non-responder 2 (NR2) before and after 7 days of saccharin consumption. l, Fold taxonomical abundance changes of selected OTUs, altered in germ-free recipients of D7 versus D1 microbiomes from R1. Dot colour same as in e, bacterial orders. Symbols (GTT) or horizontal lines (AUC), means; error bars, s.e.m. *P < 0.05, **P < 0.01, ***P < 0.001, two-way ANOVA and Bonferroni post-hoc analysis (GTT), unpaired two-sided Student t-test (AUC).
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Suez, J., Korem, T., Zeevi, D. et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature 514, 181–186 (2014). https://doi.org/10.1038/nature13793
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Robert Matthews
Interesting paper, but I'd be interested to know why the authors repeatedly state exact p-values as inequalities.
While it is common (though poor practice) to quote values are lying beneath standard significance cut-offs (typically p < 0.05, p < 0.001), stating exact p-values as "p < 0.03" simply makes no sense.
Brenda Hijmans
This article shows that glucose intolerance is induced in mice drinking a pure saccharin solution, receiving a dosage that when extrapolated to humans, is within the acceptable daily intake (ADI) according to the FDA.
Most data concerning the microbiome are however derived from mice drinking 10% solutions of commercial saccharin, consisting of 95% glucose and 5% saccharin. From the extended material it seems that the mice greatly preferred drinking this solution over normal drinking water (Extended Data Figure 3), leading to a dosage of 100 mg per day, far exceeding the ADI! This raises major concerns about toxic effects confounding the published results.
Next to this, I think it is a pity that the authors fail to refer to numerous other studies that show physiological effects of artificial sweeteners. For instance, a previous study reported adverse effects of Splenda intake in rats: decreases in the amount of beneficial intestinal bacteria, elevated fecal pH and histopathological features at dosages within the ADI^1^.
While alterations of the microbiome may have an important role in the metabolic effects of NAS, it cannot be neglected that these substances travel into the circulation and will interact with the same sweet taste receptors (T1R1/T1R3) as natural sugars, located throughout the body. Binding of artificial sweeteners to the sweet taste receptor in the intestines increases glucose absorption by upregulation of glucose transporter expression^2^. Also, studies in rats indicate that the ability of sweet tastes to signal calories may be disturbed by artificial sweetener use, through weakening of predictive relationships between sweetness and energetic value^3^.
1. Abou-Donia, M. B., El-Masry, E. M., Abdel-Rahman, A. A., McLendon, R. E. & Schiffman, S. S. Splenda alters gut microflora and increases intestinal p-glycoprotein and cytochrome p-450 in male rats. J. Toxicol. Environ. Health 71, 1415-1429 (2008).
2. Mace, O. J., Affleck, J., Patel, N. & Kellett, G. L. Sweet taste receptors in rat small intestine stimulate glucose absorption through apical GLUT2. J. Physiol. 582, 379?392 (2007).
3. Swithers, S. E., Martin, A. A. & Davidson, T. L. High-intensity sweeteners and energy balance. Physiol. Behav. 100, 55-62 (2010).
Bernadene Magnuson
The data in this study do not support the general statement that ?artificial sweeteners? induce glucose intolerance and thus cause obesity by altering the gut microbiome?.
The extensive research conducted on aspartame and sucralose has clearly demonstrated that these compounds do not affect the gut microbiota (1,2). Aspartame is completely digested into amino acids and methanol, which are absorbed in the small intestine. Neither aspartame nor its digestion products ever reach the colon; thus aspartame itself cannot affect gut microbiota. Sucralose is not digested, and passes unchanged to the large intestine; however numerous studies show pure sucralose cannot be metabolized by microflora.
So how is it possible that these 2 sweeteners reportedly altered the gut microbiota in this study? The answer ? inappropriate statistics and huge changes in overall diet composition.
Firstly, to achieve statistical significance, the authors combined all 3 different sweetener groups (n=20/group) together into one group (n=60) and compared against the combined control groups to obtain 1 statistically significant p value! So 6 groups of 20 became 2 groups of 60, making it impossible to determine which, if any, individual sweetener had a significant effect.
Secondly, the notable impact on intake of mouse chow, by adding extremely high doses of sweetener to drinking water, was ignored. Doses and food intake can only be estimated as data were reported for just 4 of the 20 mice per group and for only 3 days of the 11-week study. Doses of the sweeteners were up to 1000 times the acceptable daily intake (ADI), and consumption of mouse chow dropped by 50% in some groups in just 72 hr. Mouse chow contains fiber, protein, fat, fermentable carbohydrates and a host of other components that have repeatedly been shown to affect both gut microbiota and glycemic indices. Clearly, these dramatic changes in diet would result in changes in microbiota, and glycemic responses. Other dietary factors were similarly not considered in the human studies.
Lastly, these conclusions do not agree with the results of the extensive testing of these sweeteners required for approval, including human clinical studies conducted in healthy and diabetic participants for periods of several weeks to months, on parameters including glycemic indices and insulin (1,2). These studies must include control groups, baseline measurements, blinding, crossover designs, and appropriate statistics to ensure no effects on these parameters with continual exposure of sweeteners, at maximum expected uses.
Thus this study provides no evidence that aspartame or sucralose alters gut microbiota or glycemic response. In contrast, the observation that saccharin at high doses alters gut microbiota was known in the 80s, and contributed to the establishment of the ADI for saccharin (3). Therefore, extrapolation of findings of effects of saccharin on the gut microbiome to all artificial sweeteners has no scientific basis and overlooks well-established differences in chemistry and metabolism.
Also not mentioned are the numerous studies demonstrating that use of low calorie sweeteners, including aspartame and sucralose, are beneficial in weight loss and weight loss maintenance programs (4,5).
The allegations that ?artificial sweeteners? contribute to glucose intolerance and obesity based on studies in this report, are unfounded and should be withdrawn.
References
1. Opinion of the Scientific Committee on Food on sucralose. Available at http://ec.europa.eu/food/fs....
2. Aspartame (WHO Food Additives Series 15) available at http://www.inchem.org/docum...
3. Saccharin (WHO Food Additives Series 17) available at http://www.inchem.org/docum...
4. Miller PE, Perez V. Low-calorie sweeteners and body weight and composition: a meta-analysis of randomized controlled trials and prospective cohort studies, Am J Clin Nutr. 2014 Sep;100(3):765-77
5. Catenacci VA, Pan Z, Thomas JG, Ogden LG, Roberts SA, Wyatt HR, Wing RR, Hill JO. Low/No calorie sweetened beverage consumption in the National Weight Control Registry. Obesity (Silver Spring). 2014 Oct;22(10):2244-51.
Dr. E Replied to Bernadene Magnuson
Wow. I think I love you. HAHA! Thank you for an extremely well written response to this study. You said everything I was thinking, and then some. I'm currently conducting research re: diet soda and low/no cal sweeteners for a website. The process has been frustrating due to all the misinformation and bias, even in the peer reviewed papers. I don't understand. How does an article like this and others that twist statistics and verbiage in similar ways make it into a peer reviewed "science" journal. Maybe its time to start holding the scientific journals responsible.