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Ok * Submit * Log in * Register * Log in + Submit + Log in * Subscribe * Claim Access provided by Article|Online Now * PDF [14 MB]PDF [14 MB] * Figures + Figure Viewer + Download Figures (PPT) * Save + Add to Online LibraryPowered ByMendeley + Add to My Reading List + Export Citation + Create Citation Alert * Share Share on + Twitter + Facebook + LinkedIn + Sina Weibo * more + Reprints + Request * Top Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance * Jotham Suez ^11 Author Footnotes 11 These authors contributed equally ^, ^12 Author Footnotes 12 Present address: Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA Jotham Suez Correspondence Corresponding author Contact Footnotes 11 These authors contributed equally 12 Present address: Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Yotam Cohen ^11 Author Footnotes 11 These authors contributed equally Yotam Cohen Footnotes 11 These authors contributed equally Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Rafael Valdes-Mas Rafael Valdes-Mas Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Uria Mor Uria Mor Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Mally Dori-Bachash Mally Dori-Bachash Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Sara Federici Sara Federici Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Niv Zmora Niv Zmora Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6423906, Israel Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel Search for articles by this author * Avner Leshem Avner Leshem Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Department of Surgery, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel Search for articles by this author * Melina Heinemann Melina Heinemann Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Raquel Linevsky Raquel Linevsky Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Maya Zur Maya Zur Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Rotem Ben-Zeev Brik Rotem Ben-Zeev Brik Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Aurelie Bukimer Aurelie Bukimer Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Shimrit Eliyahu-Miller Shimrit Eliyahu-Miller Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Alona Metz Alona Metz Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Ruthy Fischbein Ruthy Fischbein Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Olga Sharov Olga Sharov Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Sergey Malitsky Sergey Malitsky Affiliations Department of Biological Services, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Maxim Itkin Maxim Itkin Affiliations Department of Biological Services, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Noa Stettner Noa Stettner Affiliations Department of Veterinary Resources, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Alon Harmelin Alon Harmelin Affiliations Department of Veterinary Resources, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Hagit Shapiro Hagit Shapiro Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Christoph K. Stein-Thoeringer Christoph K. Stein-Thoeringer Affiliations Microbiome & Cancer Division, DKFZ, Heidelberg, Germany National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany Search for articles by this author * Eran Segal Eran Segal Correspondence Corresponding author Contact Affiliations Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel Search for articles by this author * Eran Elinav ^13 Author Footnotes 13 Lead contact Eran Elinav Correspondence Corresponding author Contact Footnotes 13 Lead contact Affiliations Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel Microbiome & Cancer Division, DKFZ, Heidelberg, Germany Search for articles by this author * Show footnotesHide footnotes Author Footnotes 11 These authors contributed equally 12 Present address: Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA 13 Lead contact Published:August 19, 2022DOI:https://doi.org/10.1016/ j.cell.2022.07.016 Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance Next ArticleSustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy Advertisement Highlights * * Randomized-controlled trial on the effects of non-nutritive sweeteners in humans * * Sucralose and saccharin supplementation impairs glycemic response in healthy adults * * Personalized effects of non-nutritive sweeteners on microbiome and metabolome * * Impacts on the microbiome are causally linked to elevated glycemic response Summary Non-nutritive sweeteners (NNS) are commonly integrated into human diet and presumed to be inert; however, animal studies suggest that they may impact the microbiome and downstream glycemic responses. We causally assessed NNS impacts in humans and their microbiomes in a randomized-controlled trial encompassing 120 healthy adults, administered saccharin, sucralose, aspartame, and stevia sachets for 2 weeks in doses lower than the acceptable daily intake, compared with controls receiving sachet-contained vehicle glucose or no supplement. As groups, each administered NNS distinctly altered stool and oral microbiome and plasma metabolome, whereas saccharin and sucralose significantly impaired glycemic responses. Importantly, gnotobiotic mice conventionalized with microbiomes from multiple top and bottom responders of each of the four NNS-supplemented groups featured glycemic responses largely reflecting those noted in respective human donors, which were preempted by distinct microbial signals, as exemplified by sucralose. Collectively, human NNS consumption may induce person-specific, microbiome-dependent glycemic alterations, necessitating future assessment of clinical implications. Graphical abstract Graphical Abstract * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Keywords * microbiome * non-nutritive sweeteners * artificial sweeteners * metabolic syndrome * hyperglycemia * metagenomics * metabolomics Introduction Over the past 4 decades, the global prevalence of overweight, obesity, and hyperglycemia has markedly increased in both children and adults, constituting a considerable health threat due to the association of these conditions with type 2 diabetes and cardiovascular disease ( NCD Risk Factor Collaboration NCD-RisC, 2017 NCD Risk Factor Collaboration (NCD-RisC) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128*9 million children, adolescents, and adults. Lancet. 2017; 380: 2627-2642https://doi.org/10.1016/S0140-6736(17) 32129-3 * Abstract * Full Text * Full Text PDF * Scopus (3343) * Google Scholar , Emerging Risk Factors Collaboration et al., 2010 * Sarwar N. * Gao P. * Seshasai S.R.K. * Gobin R. * Kaptoge S. * Di Angelantonio E. * Ingelsson E. * Lawlor D.A. * Selvin E. * et al. Emerging Risk Factors Collaboration Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010; 375: 2215-2222 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (2836) * Google Scholar ), coupled with substantial economic ramifications ( Imes, and Burke, 2014 * Imes C.C. * Burke L.E. The obesity epidemic: the United States as a cautionary tale for the rest of the world. Curr. Epidemiol. Rep. 2014; 1: 82-88 * Crossref * PubMed * Google Scholar ). As sugar consumption is strongly associated with weight gain ( Hu, 2013 * Hu F.B. Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases. Obes. Rev. 2013; 14: 606-619 * Crossref * PubMed * Scopus (611) * Google Scholar ), one of the most common dietary strategies in combating obesity and hyperglycemia involves dietary sugar replacement with non-nutritive sweeteners (NNS), such as saccharin, sucralose, aspartame, acesulfame-K, and stevia, that do not contain calories and are thereby presumed to be inert and not elicit a postprandial glycemic response. This strategy is immensely popular. In a survey conducted between 2009 and 2011, 25.1% of children and 41.4% of adults in the United States reported consuming NNS, a marked increase compared with 1999 ( Sylvetsky et al., 2017a * Sylvetsky A.C. * Jin Y. * Clark E.J. * Welsh J.A. * Rother K.I. * Talegawkar S.A. Consumption of low-calorie sweeteners among children and adults in the United States. J. Acad. Nutr. Diet. 2017; 117: 441-448.e2 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (132) * Google Scholar ). Over 50% of children reported consumption of NNS in a multi-national study ( Katzmarzyk et al., 2016 * Katzmarzyk P.T. * Broyles S.T. * Champagne C.M. * Chaput J.P. * Fogelholm M. * Hu G. * Kuriyan R. * Kurpad A. * Lambert E.V. * Maia J. * et al. Relationship between soft drink consumption and obesity in 9-11 years old children in a multi-national study. Nutrients. 2016; 8: 770 * Crossref * Scopus (30) * Google Scholar ), whereas countries enforcing labeling of sugar-containing products observe a high concomitant consumption of NNS-containing products ( Martinez et al., 202 0 * Martinez X. * Zapata Y. * Pinto V. * Cornejo C. * Elbers M. * van der Graaf M.V. * Villarroel L. * Hodgson M.I. * Rigotti A. * Echeverria G. Intake of non-nutritive sweeteners in Chilean children after enforcement of a new food labeling law that regulates added sugar content in processed foods. Nutrients. 2020; 12: 1594 * Crossref * Scopus (14) * Google Scholar ). Nonetheless, the efficacy of this strategy remains uncertain. Although some randomized-controlled trials (RCTs) report improvement in metabolic markers in subjects supplemented with NNS ( Blackburn et al., 1997 * Blackburn G.L. * Kanders B.S. * Lavin P.T. * Keller S.D. * Whatley J. The effect of aspartame as part of a multidisciplinary weight-control program on short- and long-term control of body weight. Am. J. Clin. Nutr. 1997; 65: 409-418 * Crossref * PubMed * Scopus (155) * Google Scholar ; Ebbeling et al., 2020 * Ebbeling C.B. * Feldman H.A. * Steltz S.K. * Quinn N.L. * Robinson L.M. * Ludwig D.S. Effects of sugar-sweetened, artificially sweetened, and unsweetened beverages on cardiometabolic risk factors, body composition, and sweet taste preference: a randomized controlled trial. J. Am. Heart Assoc. 2020; 9: e015668 * Crossref * PubMed * Scopus (13) * Google Scholar ; Katan et al., 2016 * Katan M.B. * de Ruyter J.C. * Kuijper L.D.J. * Chow C.C. * Hall K.D. * Olthof M.R. Impact of masked replacement of sugar-sweetened with sugar-free beverages on body weight increases with initial BMI: secondary analysis of data from an 18 month double-blind trial in children. PLoS One. 2016; 11: e0159771 * Crossref * PubMed * Scopus (21) * Google Scholar ; Masic et al., 2017 * Masic U. * Harrold J.A. * Christiansen P. * Cuthbertson D.J. * Hardman C.A. * Robinson E. * Halford J.C.G. Effects of non-nutritive sWeetened beverages on appetITe during aCtive weigHt loss (SWITCH): protocol for a randomized, controlled trial assessing the effects of non-nutritive sweetened beverages compared to water during a 12-week weight loss period and a follow up weight maintenance period. Contemp. Clin. Trials. 2017; 53: 80-88 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (0) * Google Scholar ; Miller, and Perez, 2014 * Miller P.E. * 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; 100: 765-777 * Crossref * PubMed * Scopus (189) * Google Scholar ; Tate et al., 2012 * Tate D.F. * Turner-McGrievy G. * Lyons E. * Stevens J. * Erickson K. * Polzien K. * Diamond M. * Wang X. * Popkin B. Replacing caloric beverages with water or diet beverages for weight loss in adults: main results of the Choose Healthy Options Consciously Everyday (CHOICE) randomized clinical trial. Am. J. Clin. Nutr. 2012; 95: 555-563 * Crossref * PubMed * Scopus (255) * Google Scholar ), other RCTs report neither a detrimental nor a beneficial effect ( Ahmad et al., 2020a * Ahmad S.Y. * Friel J.K. * MacKay D.S. The effect of the artificial sweeteners on glucose metabolism in healthy adults: a randomized, double-blinded, crossover clinical trial. Appl. Physiol. Nutr. Metab. 2020; 45: 606-612 * Crossref * PubMed * Scopus (13) * Google Scholar ; Thomson et al., 2019 * Thomson P. * Santibanez R. * Aguirre C. * Galgani J.E. * Garrido D. Short-term impact of sucralose consumption on the metabolic response and gut microbiome of healthy adults. Br. J. Nutr. 2019; 122: 856-862 * Crossref * PubMed * Scopus (0) * Google Scholar ) and do not support the intended benefits of this approach ( Azad et al., 2017 * Azad M.B. * Abou-Setta A.M. * Chauhan B.F. * Rabbani R. * Lys J. * Copstein L. * Mann A. * Jeyaraman M.M. * Reid A.E. * Fiander M. * et al. Chronic sucralose consumpt. CMAJ. 2017; 189: E929-E939 * Crossref * PubMed * Scopus (0) * Google Scholar ; Lohner et al., 2020 * Lohner S. * Kuellenberg de Gaudry D. * Toews I. * Ferenci T. * Meerpohl J.J. Non-nutritive sweeteners for diabetes mellitus. Cochrane Database Syst. Rev. 2020; 5: CD012885 * PubMed * Google Scholar ; Toews et al., 2019 * Toews I. * Lohner S. * Kullenberg de Gaudry D. * Sommer H. * Meerpohl J.J. Association between intake of non-sugar sweeteners and health outcomes: systematic review and meta-analyses of randomised and non-randomised controlled trials and observational studies. BMJ. 2019; 364: k4718 * Crossref * PubMed * Scopus (113) * Google Scholar ). Furthermore, some cohort studies ( Azad et al., 2017 * Azad M.B. * Abou-Setta A.M. * Chauhan B.F. * Rabbani R. * Lys J. * Copstein L. * Mann A. * Jeyaraman M.M. * Reid A.E. * Fiander M. * et al. Chronic sucralose consumpt. CMAJ. 2017; 189: E929-E939 * Crossref * PubMed * Scopus (0) * Google Scholar , Azad et al., 2020 * Azad M.B. * Archibald A. * Tomczyk M.M. * Head A. * Cheung K.G. * de Souza R.J. * Becker A.B. * Mandhane P.J. * Turvey S.E. * Moraes T.J. * et al. Nonnutritive sweetener consumption during pregnancy, adiposity, and adipocyte differentiation in offspring: evidence from humans, mice, and cells. Int. J. Obes. (Lond). 2020; 44: 2137-2148 * Crossref * PubMed * Scopus (10) * Google Scholar ; Romo-Romo et al., 2016 * Romo-Romo A. * Aguilar-Salinas C.A. * Brito-Cordova G.X. * Gomez Diaz R.A. * Vilchis Valentin D. * Almeda-Valdes P. Effects of the non-nutritive sweeteners on glucose metabolism and appetite regulating hormones: systematic review of observational prospective studies and clinical trials. PLoS One. 2016; 11: e0161264 * Crossref * PubMed * Scopus (64) * Google Scholar ; Swithers, 2013 * Swithers S.E. Artificial sweeteners produce the counterintuitive effect of inducing metabolic derangements. Trends Endocrinol. Metab. 2013; 24: 431-441 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (259) * Google Scholar ) and RCTs ( Bueno-Hernandez et al., 2020 * Bueno-Hernandez N. * Esquivel-Velazquez M. * Alcantara-Suarez R. * Gomez-Arauz A.Y. * Espinosa-Flores A.J. * de Leon-Barrera K.L. * Mendoza-Martinez V.M. * Sanchez Medina G.A. * Leon-Hernandez M. * Ruiz-Barranco A. * et al. Chronic sucralose consumption induces elevation of serum insulin in young healthy adults: a randomized, double blind, controlled trial. Nutr. J. 2020; 19: 32 * Crossref * PubMed * Scopus (8) * Google Scholar ; Dalenberg et al., 2020 * Dalenberg J.R. * Patel B.P. * Denis R. * Veldhuizen M.G. * Nakamura Y. * Vinke P.C. * Luquet S. * Small D.M. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metab. 2020; 31: 493-502.e7 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (50) * Google Scholar ; Lertrit et al., 2018 * Lertrit A. * Srimachai S. * Saetung S. * Chanprasertyothin S. * Chailurkit L.O. * Areevut C. * Katekao P. * Ongphiphadhanakul B. * Sriphrapradang C. Effects of sucralose on insulin and glucagon-like peptide-1 secretion in healthy subjects: a randomized, double-blind, placebo-controlled trial. Nutrition. 2018; 55-56: 125-130 * Crossref * PubMed * Scopus (42) * Google Scholar ; Mendez-Garcia et al., 2022 * Mendez-Garcia L.A. * Bueno-Hernandez N. * Cid-Soto M.A. * De Leon K.L. * Mendoza-Martinez V.M. * Espinosa-Flores A.J. * Carrero-Aguirre M. * Esquivel-Velazquez M. * Leon-Hernandez M. * Viurcos-Sanabria R. * et al. Ten-week sucralose consumption induces gut dysbiosis and altered glucose and insulin levels in healthy young adults. Microorganisms. 2022; 10: 434 * Crossref * PubMed * Scopus (1) * Google Scholar ; Romo-Romo et al., 2018 * Romo-Romo A. * Aguilar-Salinas C.A. * Brito-Cordova G.X. * Gomez-Diaz R.A. * Almeda-Valdes P. Sucralose decreases insulin sensitivity in healthy subjects: a randomized controlled trial. Am. J. Clin. Nutr. 2018; 108: 485-491 * Crossref * PubMed * Scopus (38) * Google Scholar ) counterintuitively suggest that NNS may even contribute to the obesity and diabetes pandemic in some contexts. As many of the studies associating NNS with negative impacts on human health are observational, it is often difficult to interpret their findings due to reverse causality (i.e., whether NNS cause weight gain and hyperglycemia, or alternatively whether individuals with these conditions consume NNS). The heterogeneity in outcomes and methodology between RCTs further complicates interpretation. In the absence of strong evidence for causality and a clear mechanism demonstrating how "metabolically inert" substances can affect human metabolism, consumption of NNS is still widely endorsed by clinicians and dietitians for adults ( Gardner et al., 2012 * Gardner C. * Wylie-Rosett J. * Gidding S.S. * Steffen L.M. * Johnson R.K. * Reader D. * Lichtenstein A.H. Nonnutritive sweeteners: current use and health perspectives: a scientific statement from the American Heart Association and the American Diabetes Association. Diabetes Care. 2012; 126: 1798-1808 * Crossref * Scopus (157) * Google Scholar ), although a more cautious approach has been lately recommended for children ( Johnson et al., 2018 * Johnson R.K. * Lichtenstein A.H. * Anderson C.A.M. * Carson J.A. * Despres J.P. * Hu F.B. * Kris-Etherton P.M. * Otten J.J. * Towfighi A. * Wylie-Rosett J. * et al. Low-calorie sweetened beverages and cardiometabolic health: A science advisory from the American Heart Association. Circulation. 2018; 138: e126-e140https://doi.org/10.1161/ CIR.0000000000000569 * Crossref * PubMed * Scopus (73) * Google Scholar ). To partially circumvent the limitations of human studies, feeding trials in animals are commonly used to causally link NNS intake with an effect on cardiometabolic diseases. However, although animal studies are less heterogeneous than human RCTs, some reported an adverse impact of NNS on metabolic health ( Abou-Donia et al., 2008 * 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 A. 2008; 71: 1415-1429 * Crossref * PubMed * Scopus (210) * Google Scholar ; Bian et al., 2017a * Bian X. * Tu P. * Chi L. * Gao B. * Ru H. * Lu K. Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions. Food Chem. Toxicol. 2017; 107: 530-539 * Crossref * PubMed * Scopus (81) * Google Scholar , Bian et al., 2017b * Bian X. * Chi L. * Gao B. * Tu P. * Ru H. * Lu K. The artificial sweetener acesulfame potassium affects the gut microbiome and body weight gain in CD-1 mice. PLoS One. 2017; 12: e0178426 * Crossref * PubMed * Scopus (100) * Google Scholar ; Collison et al., 2012 * Collison K.S. * Makhoul N.J. * Zaidi M.Z. * Saleh S.M. * Andres B. * Inglis A. * Al-Rabiah R. * Al-Mohanna F.A. Gender dimorphism in aspartame-induced impairment of spatial cognition and insulin sensitivity. PLoS One. 2012; 7: e31570 * Crossref * PubMed * Scopus (37) * Google Scholar ; Feijo et al., 2013 * Feijo F.M. * Ballard C.R. * Foletto K.C. * Batista B.A.M. * Neves A.M. * Ribeiro M.F.M. * Bertoluci M.C. Saccharin and aspartame, compared with sucrose, induce greater weight gain in adult Wistar rats, at similar total caloric intake levels. Appetite. 2013; 60: 203-207 * Crossref * PubMed * Scopus (73) * Google Scholar ; Gul et al., 2017 * Gul S.S. * Hamilton A.R.L. * Munoz A.R. * Phupitakphol T. * Liu W. * Hyoju S.K. * Economopoulos K.P. * Morrison S. * Hu D. * Zhang W. * et al. Inhibition of the gut enzyme intestinal alkaline phosphatase may explain how aspartame promotes glucose intolerance and obesity in mice. Appl. Physiol. Nutr. Metab. 2017; 42: 77-83 * Crossref * PubMed * Scopus (28) * Google Scholar ; Leibowitz et al., 2018 * Leibowitz A. * Bier A. * Gilboa M. * Peleg E. * Barshack I. * Grossman E. Saccharin increases fasting blood glucose but not liver insulin resistance in comparison to a high fructose-fed rat model. Nutrients. 2018; 10: 341 * Crossref * Scopus (9) * Google Scholar ; Mitsutomi et al., 2014 * Mitsutomi K. * Masaki T. * Shimasaki T. * Gotoh K. * Chiba S. * Kakuma T. * Shibata H. Effects of a nonnutritive sweetener on body adiposity and energy metabolism in mice with diet-induced obesity. Metabolism. 2014; 63: 69-78 * Abstract * Full Text * Full Text PDF * PubMed * Google Scholar ; Nettleton et al., 2020 * Nettleton J.E. * Cho N.A. * Klancic T. * Nicolucci A.C. * Shearer J. * Borgland S.L. * Johnston L.A. * Ramay H.R. * Noye Tuplin E. * Chleilat F. * et al. Maternal low-dose aspartame and stevia consumption with an obesogenic diet alters metabolism, gut microbiota and mesolimbic reward system in rat dams and their offspring. Gut. 2020; 69: 1807-1817 * Crossref * PubMed * Scopus (22) * Google Scholar ; Olivier-Van Stichelen et al., 2019 * Olivier-Van Stichelen S. * Rother K.I. * Hanover J.A. Maternal exposure to non-nutritive sweeteners impacts progeny's metabolism and microbiome. Front. Microbiol. 2019; 10: 1360 * Crossref * PubMed * Scopus (39) * Google Scholar ; Otero-Losada et al., 2014 * Otero-Losada M. * Cao G. * Mc Loughlin S. * Rodriguez-Granillo G. * Ottaviano G. * Milei J. Rate of atherosclerosis progression in ApoE-/- mice long after discontinuation of cola beverage drinking. PLoS One. 2014; 9: e89838 * Crossref * PubMed * Scopus (8) * Google Scholar ; Palmnas et al., 2014 * Palmnas M.S.A. * Cowan T.E. * Bomhof M.R. * Su J. * Reimer R.A. * Vogel H.J. * Hittel D.S. * Shearer J. Low-dose aspartame consumption differentially affects gut microbiota-host metabolic interactions in the diet-induced obese rat. PLoS One. 2014; 9: e109841 * Crossref * PubMed * Scopus (0) * Google Scholar ; von Poser Toigo et al., 2015 * von Poser Toigo E. * Huffell A.P. * Mota C.S. * Bertolini D. * Pettenuzzo L.F. * Dalmaz C. Metabolic and feeding behavior alterations provoked by prenatal exposure to aspartame. Appetite. 2015; 87: 168-174 * Crossref * PubMed * Scopus (29) * Google Scholar ; Shi et al., 2019 * Shi Q. * Cai L. * Jia H. * Zhu X. * Chen L. * Deng S. Low intake of digestible carbohydrates ameliorates duodenal absorption of carbohydrates in mice with glucose metabolism disorders induced by artificial sweeteners. J. Sci. Food Agric. 2019; 99: 4952-4962 * Crossref * PubMed * Scopus (4) * Google Scholar ; Suez et al., 2014 * Suez J. * Korem T. * Zeevi D. * Zilberman-Schapira G. * Thaiss C.A. * Maza O. * Israeli D. * Zmora N. * Gilad S. * Weinberger A. * et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 * Crossref * PubMed * Scopus (1108) * Google Scholar ; Swithers et al., 2008 * Swithers S.E. * Baker C.R. * Mccurley M. * Davidson T.L. Persistent effects of high-intensity sweeteners on body weight gain in rats. Appetite. 2008; 51: 403 * Crossref * Google Scholar ; Uebanso et al., 2017 * Uebanso T. * Ohnishi A. * Kitayama R. * Yoshimoto A. * Nakahashi M. * Shimohata T. * Mawatari K. * Takahashi A. Effects of low-dose non-caloric sweetener consumption on gut microbiota in mice. Nutrients. 2017; 9: 560https://doi.org/10.3390/nu9060560 * Crossref * Scopus (0) * Google Scholar ), whereas others reported a beneficial effect or no effect ( Bailey et al., 1997 * Bailey C.J. * Day C. * Knapper J.M. * Turner S.L. * Flatt P.R. Antihyperglycaemic effect of saccharin in diabetic ob/ob mice. Br. J. Pharmacol. 1997; 120: 74-78 * Crossref * PubMed * Scopus (23) * Google Scholar ; Parlee et al., 2014 * Parlee S.D. * Simon B.R. * Scheller E.L. * Alejandro E.U. * Learman B.S. * Krishnan V. * Bernal-Mizrachi E. * MacDougald O.A. Administration of saccharin to neonatal mice influences body composition of adult males and reduces body weight of females. Endocrinology. 2014; 155: 1313-1326 * Crossref * PubMed * Scopus (16) * Google Scholar ; Risdon et al., 2020 * Risdon S. * Paillargue M. * Meyer G. * Walther G. Non-nutritive sweetener sucralose chronic consumption is able to reduce the deleterious effect of high-fat diet on body composition, glucose metabolism and vascular function in C57BL/6JR mice. Arch. Cardiovasc. Dis. Suppl. 2020; 12: 208 * Google Scholar ; Serrano et al., 2021 * Serrano J. * Smith K.R. * Crouch A.L. * Sharma V. * Yi F. * Vargova V. * LaMoia T.E. * Dupont L.M. * Serna V. * Tang F. * et al. High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice. Microbiome. 2021; 9: 11 * Crossref * PubMed * Scopus (11) * Google Scholar ; Tovar et al., 2017 * Tovar A.P. * Navalta J.W. * Kruskall L.J. * Young J.C. The effect of moderate consumption of non-nutritive sweeteners on glucose tolerance and body composition in rats. Appl. Physiol. Nutr. Metab. 2017; 42: 1225-1227 * Crossref * PubMed * Scopus (2) * Google Scholar ). This noted and often confusing heterogeneity between trials could potentially be resolved through consideration of the gut microbiome. The human gastrointestinal tract harbors trillions of microorganisms that play critical roles in multiple aspects of human physiology and pathologies, including cardiometabolic health ( Fan, and Pedersen, 2021 * Fan Y. * Pedersen O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 2021; 19: 55-71 * Crossref * PubMed * Scopus (560) * Google Scholar ). Importantly, the assemblage of microorganisms varies between individuals (and between animals in different research vivaria), leading to personalized responses to diets ( Berry et al., 2020 * Berry S.E. * Valdes A.M. * Drew D.A. * Asnicar F. * Mazidi M. * Wolf J. * Capdevila J. * Hadjigeorgiou G. * Davies R. * Al Khatib H. * et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 2020; 26: 964-973 * Crossref * PubMed * Scopus (153) * Google Scholar ; Korem et al., 2017 * Korem T. * Zeevi D. * Zmora N. * Weissbrod O. * Bar N. * Lotan-Pompan M. * Avnit-Sagi T. * Kosower N. * Malka G. * Rein M. * et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 2017; 25: 1243-1253.e5 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (143) * Google Scholar ; Kovatcheva-Datchary et al., 2015 * Kovatcheva-Datchary P. * Nilsson A. * Akrami R. * Lee Y.S. * De Vadder F. * Arora T. * Hallen A. * Martens E. * Bjorck I. * Backhed F. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015; 22: 971-982 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (762) * Google Scholar ; Spencer et al., 2011 * Spencer M.D. * Hamp T.J. * Reid R.W. * Fischer L.M. * Zeisel S.H. * Fodor A.A. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology. 2011; 140: 976-986 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (414) * Google Scholar ; Zeevi et al., 2015 * Zeevi D. * Korem T. * Zmora N. * Israeli D. * Rothschild D. * Weinberger A. * Ben-Yacov O. * Lador D. * Avnit-Sagi T. * Lotan-Pompan M. * et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015; 163: 1079-1094 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (1202) * Google Scholar ) and therapeutics ( Ananthakrishnan et al., 2017 * Ananthakrishnan A.N. * Luo C. * Yajnik V. * Khalili H. * Garber J.J. * Stevens B.W. * Cleland T. * Xavier R.J. Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases. Cell Host Microbe. 2017; 21: 603-610.e3 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (180) * Google Scholar ; Gopalakrishnan et al., 2018 * Gopalakrishnan V. * Spencer C.N. * Nezi L. * Reuben A. * Andrews M.C. * Karpinets T.V. * Prieto P.A. * Vicente D. * Hoffman K. * Wei S.C. * et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018; 359: 97-103 * Crossref * PubMed * Scopus (1931) * Google Scholar ; Matson et al., 2018 * Matson V. * Fessler J. * Bao R. * Chongsuwat T. * Zha Y. * Alegre M.L. * Luke J.J. * Gajewski T.F. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 2018; 359: 104-108 * Crossref * PubMed * Scopus (1239) * Google Scholar ; Routy et al., 2018 * Routy B. * Le Chatelier E. * Derosa L. * Duong C.P.M. * Alou M.T. * Daillere R. * Fluckiger A. * Messaoudene M. * Rauber C. * Roberti M.P. * et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018; 359: 91-97 * Crossref * PubMed * Scopus (2267) * Google Scholar ; Zmora et al., 2018 * Zmora N. * Zilberman-Schapira G. * Suez J. * Mor U. * Dori-Bachash M. * Bashiardes S. * Kotler E. * Zur M. * Regev-Lehavi D. * Brik R.B.-Z. * et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell. 2018; 174: 1388-1405.e21 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (602) * Google Scholar ). Indeed, NNS have been shown to affect microbial growth in culture ( Harpaz et al., 2018 * Harpaz D. * Yeo L.P. * Cecchini F. * Koon T.H.P. * Kushmaro A. * Tok A.I.Y. * Marks R.S. * Eltzov E. Measuring artificial sweeteners toxicity using a bioluminescent bacterial panel. Molecules. 2018; 23: 2454 * Crossref * Scopus (32) * Google Scholar ; Omran et al., 2013 * Omran A. * Ahearn G. * Bowers D. * Swenson J. * Coughlin C. Metabolic effects of sucralose on environmental bacteria. J. Toxicol. 2013; 2013: 372986 * Crossref * PubMed * Scopus (23) * Google Scholar ; Rettig et al., 2014 * Rettig S. * Tenewitz J. * Ahearn G. * Coughlin C. Sucralose causes a concentration dependent metabolic inhibition of the gut flora Bacteroides, B. fragilis and B. uniformis not observed in the Firmicutes, E. faecalis and C. sordellii (1118.1). FASEB J. 2014; 28https://doi.org/10.1096/ fasebj.28.1_supplement.1118.1 * Crossref * Google Scholar ; Wang et al., 2018 * Wang Q.P. * Browman D. * Herzog H. * Neely G.G. Non-nutritive sweeteners possess a bacteriostatic effect and alter gut microbiota in mice. PLoS One. 2018; 13: e0199080 * PubMed * Google Scholar ) and modulate the microbiome of animals ( Abou-Donia et al., 2008 * 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 A. 2008; 71: 1415-1429 * Crossref * PubMed * Scopus (210) * Google Scholar ; Anderson, and Kirkland, 1980 * Anderson R.L. * Kirkland J.J. The effect of sodium saccharin in the diet on caecal microflora. Food Cosmet. Toxicol. 1980; 18: 353-355 * Crossref * PubMed * Google Scholar ; Bian et al., 2017a * Bian X. * Tu P. * Chi L. * Gao B. * Ru H. * Lu K. Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions. Food Chem. Toxicol. 2017; 107: 530-539 * Crossref * PubMed * Scopus (81) * Google Scholar , Bian et al., 2017b * Bian X. * Chi L. * Gao B. * Tu P. * Ru H. * Lu K. The artificial sweetener acesulfame potassium affects the gut microbiome and body weight gain in CD-1 mice. PLoS One. 2017; 12: e0178426 * Crossref * PubMed * Scopus (100) * Google Scholar , Bian et al., 2017c * Bian X. * Chi L. * Gao B. * Tu P. * Ru H. * Lu K. Gut microbiome response to sucralose and its potential role in inducing liver inflammation in mice. Front. Physiol. 2017; 8: 487https://doi.org/10.3389/fphys.2017.00487 * Crossref * PubMed * Scopus (114) * Google Scholar ; Cheng et al., 2021 * Cheng X. * Guo X. * Huang F. * Lei H. * Zhou Q. * Song C. Effect of different sweeteners on the oral microbiota and immune system of Sprague Dawley rats. AMB Express. 2021; 11: 8 * Crossref * PubMed * Scopus (1) * Google Scholar ; Chi et al., 2018 * Chi L. * Bian X. * Gao B. * Tu P. * Lai Y. * Ru H. * Lu K. Effects of the artificial sweetener neotame on the gut microbiome and fecal metabolites in mice. Molecules. 2018; 23: 367 * Crossref * Scopus (40) * Google Scholar ; Dai et al., 2021 * Dai X. * Wang C. * Guo Z. * Li Y. * Liu T. * Jin G. * Wang S. * Wang B. * Jiang K. * Cao H. Maternal sucralose exposure induces Paneth cell defects and exacerbates gut dysbiosis of progeny mice. Food Funct. 2021; 12: 12634-12646 * Crossref * PubMed * Google Scholar ; Daly et al., 2014 * Daly K. * Darby A.C. * Hall N. * Nau A. * Bravo D. * Shirazi-Beechey S.P. Dietary supplementation with lactose or artificial sweetener enhances swine gut Lactobacillus population abundance. Br. J. Nutr. 2014; 111: S30-S35 * Crossref * PubMed * Scopus (0) * Google Scholar ; Guo et al., 2021 * Guo M. * Liu X. * Tan Y. * Kang F. * Zhu X. * Fan X. * Wang C. * Wang R. * Liu Y. * Qin X. * et al. Sucralose enhances the susceptibility to dextran sulfate sodium (DSS) induced colitis in mice with changes in gut microbiota. Food Funct. 2021; 12: 9380-9390 * Crossref * PubMed * Google Scholar ; Hanawa et al., 2021 * Hanawa Y. * Higashiyama M. * Kurihara C. * Tanemoto R. * Ito S. * Mizoguchi A. * Nishii S. * Wada A. * Inaba K. * Sugihara N. * et al. Acesulfame potassium induces dysbiosis and intestinal injury with enhanced lymphocyte migration to intestinal mucosa. J. Gastroenterol. Hepatol. 2021; 36: 3140-3148 * Crossref * PubMed * Scopus (1) * Google Scholar ; Harrington et al., 2022 * Harrington V. * Lau L. * Crits-Christoph A. * Suez J. Interactions of non-nutritive artificial sweeteners with the microbiome in metabolic syndrome. Immunometabolism. 2022; 4: e220012 * Crossref * PubMed * Google Scholar ; Li et al., 2021 * Li J. * Zhu S. * Lv Z. * Dai H. * Wang Z. * Wei Q. * Hamdard E. * Mustafa S. * Shi F. * Fu Y. Drinking water with saccharin sodium alters the microbiota-gut-hypothalamus axis in guinea pig. Animals (Basel). 2021; 11: 1875 * Crossref * PubMed * Scopus (2) * Google Scholar ; Lyte et al., 2016 * Lyte M. * Fodor A.A. * Chapman C.D. * Martin G.G. * Perez-Chanona E. * Jobin C. * Dess N.K. Gut microbiota and a selectively bred taste phenotype: A novel model of microbiome-behavior relationships. Psychosom. Med. 2016; 78: 610-619 * Crossref * PubMed * Scopus (18) * Google Scholar ; Martinez-Carrillo et al., 2019 * Martinez-Carrillo B.E. * Rosales-Gomez C.A. * Ramirez-Duran N. * Resendiz-Albor A.A. * Escoto-Herrera J.A. * Mondragon-Velasquez T. * Valdes-Ramos R. * Castillo-Cardiel A. Effect of chronic consumption of sweeteners on microbiota and immunity in the small intestine of young mice. Int. J. Food Sci. 2019; 2019: 9619020 * Crossref * PubMed * Scopus (9) * Google Scholar ; Nettleton et al., 2019 * Nettleton J.E. * Klancic T. * Schick A. * Choo A.C. * Shearer J. * Borgland S.L. * Chleilat F. * Mayengbam S. * Reimer R.A. Low-dose stevia (rebaudioside A) consumption perturbs gut microbiota and the mesolimbic dopamine reward system. Nutrients. 2019; 11: 1248 * Crossref * Scopus (32) * Google Scholar , Nettleton et al., 2020 * Nettleton J.E. * Cho N.A. * Klancic T. * Nicolucci A.C. * Shearer J. * Borgland S.L. * Johnston L.A. * Ramay H.R. * Noye Tuplin E. * Chleilat F. * et al. Maternal low-dose aspartame and stevia consumption with an obesogenic diet alters metabolism, gut microbiota and mesolimbic reward system in rat dams and their offspring. Gut. 2020; 69: 1807-1817 * Crossref * PubMed * Scopus (22) * Google Scholar ; Olivier-Van Stichelen et al., 2019 * Olivier-Van Stichelen S. * Rother K.I. * Hanover J.A. Maternal exposure to non-nutritive sweeteners impacts progeny's metabolism and microbiome. Front. Microbiol. 2019; 10: 1360 * Crossref * PubMed * Scopus (39) * Google Scholar ; Palmnas et al., 2014 * Palmnas M.S.A. * Cowan T.E. * Bomhof M.R. * Su J. * Reimer R.A. * Vogel H.J. * Hittel D.S. * Shearer J. Low-dose aspartame consumption differentially affects gut microbiota-host metabolic interactions in the diet-induced obese rat. PLoS One. 2014; 9: e109841 * Crossref * PubMed * Scopus (0) * Google Scholar ; Rodriguez-Palacios et al., 2018 * Rodriguez-Palacios A. * Harding A. * Menghini P. * Himmelman C. * Retuerto M. * Nickerson K.P. * Lam M. * Croniger C.M. * McLean M.H. * Durum S.K. * et al. The artificial sweetener Splenda promotes gut Proteobacteria, dysbiosis, and myeloperoxidase reactivity in Crohn's disease-like ileitis. Inflamm. Bowel Dis. 2018; 24: 1005-1020 * Crossref * PubMed * Scopus (91) * Google Scholar ; Sanchez-Tapia et al., 2020 * Sanchez-Tapia M. * Miller A.W. * Granados-Portillo O. * Tovar A.R. * Torres N. The development of metabolic endotoxemia is dependent on the type of sweetener and the presence of saturated fat in the diet. Gut Microbes. 2020; 12: 1801301 * Crossref * PubMed * Scopus (0) * Google Scholar ; Sunderhauf et al., 2020 * Sunderhauf A. * Pagel R. * Kunstner A. * Wagner A.E. * Rupp J. * Ibrahim S.M. * Derer S. * Sina C. Saccharin supplementation inhibits bacterial growth and reduces experimental colitis in mice. Nutrients. 2020; 12: 1122 * Crossref * Scopus (7) * Google Scholar ; Uebanso et al., 2017 * Uebanso T. * Ohnishi A. * Kitayama R. * Yoshimoto A. * Nakahashi M. * Shimohata T. * Mawatari K. * Takahashi A. Effects of low-dose non-caloric sweetener consumption on gut microbiota in mice. Nutrients. 2017; 9: 560https://doi.org/10.3390/nu9060560 * Crossref * Scopus (0) * Google Scholar ; Wang et al., 2018 * Wang Q.P. * Browman D. * Herzog H. * Neely G.G. Non-nutritive sweeteners possess a bacteriostatic effect and alter gut microbiota in mice. PLoS One. 2018; 13: e0199080 * PubMed * Google Scholar ; Zheng et al., 2022 * Zheng Z. * Xiao Y. * Ma L. * Lyu W. * Peng H. * Wang X. * Ren Y. * Li J. Low dose of sucralose alter gut microbiome in mice. Front. Nutr. 2022; 9: 848392 * Crossref * PubMed * Scopus (1) * Google Scholar ). Furthermore, an NNS-modulated microbiome is sufficient to promote glucose intolerance in germ-free (GF) mice ( Nettleton et al., 2020 * Nettleton J.E. * Cho N.A. * Klancic T. * Nicolucci A.C. * Shearer J. * Borgland S.L. * Johnston L.A. * Ramay H.R. * Noye Tuplin E. * Chleilat F. * et al. Maternal low-dose aspartame and stevia consumption with an obesogenic diet alters metabolism, gut microbiota and mesolimbic reward system in rat dams and their offspring. Gut. 2020; 69: 1807-1817 * Crossref * PubMed * Scopus (22) * Google Scholar ; Suez et al., 2014 * Suez J. * Korem T. * Zeevi D. * Zilberman-Schapira G. * Thaiss C.A. * Maza O. * Israeli D. * Zmora N. * Gilad S. * Weinberger A. * et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 * Crossref * PubMed * Scopus (1108) * Google Scholar ), providing a possible causal link between NNS, microbiome, and metabolic health of the host. Although a small pilot study in humans suggested that the microbiome may constitute a potential determinant of a negative effect of saccharin on glycemic response in some individuals ( Suez et al., 2014 * Suez J. * Korem T. * Zeevi D. * Zilberman-Schapira G. * Thaiss C.A. * Maza O. * Israeli D. * Zmora N. * Gilad S. * Weinberger A. * et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 * Crossref * PubMed * Scopus (1108) * Google Scholar ), there is a dearth of evidence on the effects of NNS on the human microbiome, and the few available studies are inconclusive, with some suggesting an effect ( Frankenfeld et al., 2015 * Frankenfeld C.L. * Sikaroodi M. * Lamb E. * Shoemaker S. * Gillevet P.M. High-intensity sweetener consumption and gut microbiome content and predicted gene function in a cross-sectional study of adults in the United States. Ann. Epidemiol. 2015; 25 (42.e4): 736 * Crossref * PubMed * Google Scholar ; Laforest-Lapointe et al., 2021 * Laforest-Lapointe I. * Becker A.B. * Mandhane P.J. * Turvey S.E. * Moraes T.J. * Sears M.R. * Subbarao P. * Sycuro L.K. * Azad M.B. * Arrieta M.-C. Maternal consumption of artificially sweetened beverages during pregnancy is associated with infant gut microbiota and metabolic modifications and increased infant body mass index. Gut Microbes. 2021; 13: 1-15 * Crossref * PubMed * Scopus (0) * Google Scholar ; Mendez-Garcia et al., 2022 * Mendez-Garcia L.A. * Bueno-Hernandez N. * Cid-Soto M.A. * De Leon K.L. * Mendoza-Martinez V.M. * Espinosa-Flores A.J. * Carrero-Aguirre M. * Esquivel-Velazquez M. * Leon-Hernandez M. * Viurcos-Sanabria R. * et al. Ten-week sucralose consumption induces gut dysbiosis and altered glucose and insulin levels in healthy young adults. Microorganisms. 2022; 10: 434 * Crossref * PubMed * Scopus (1) * Google Scholar ), as opposed to others ( Ahmad et al., 2020b * Ahmad S.Y. * Friel J. * Mackay D. The effects of non-nutritive artificial sweeteners, aspartame and sucralose, on the gut microbiome in healthy adults: secondary outcomes of a randomized double-blinded crossover clinical trial. Nutrients. 2020; 12: 3408 * Crossref * Scopus (12) * Google Scholar ; Serrano et al., 2021 * Serrano J. * Smith K.R. * Crouch A.L. * Sharma V. * Yi F. * Vargova V. * LaMoia T.E. * Dupont L.M. * Serna V. * Tang F. * et al. High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice. Microbiome. 2021; 9: 11 * Crossref * PubMed * Scopus (11) * Google Scholar ; Thomson et al., 2019 * Thomson P. * Santibanez R. * Aguirre C. * Galgani J.E. * Garrido D. Short-term impact of sucralose consumption on the metabolic response and gut microbiome of healthy adults. Br. J. Nutr. 2019; 122: 856-862 * Crossref * PubMed * Scopus (0) * Google Scholar ). Notably, most of these studies were limited in their ability to screen and exclude individuals already incorporating NNS into their diets, and their microbiome was profiled using 16S rRNA gene sequencing or qPCR of target microbes, which may lack sufficient resolution to determine functional and species-level effects of NNS on the microbiome. Here, we present the result of a multi-arm RCT assessing NNS effects on human metabolic health and the microbiome. We demonstrate that both sucralose and saccharin supplementation impairs glycemic responses in strictly non-NNS-consuming healthy volunteers, an effect that is not observed in non-supplemented control groups. Four different NNS functionally alter the microbiome. Importantly, by performing extensive fecal transplantation of human microbiomes into GF mice, we demonstrate a causal and individualized link between NNS-altered microbiomes and glucose intolerance developing in non-NNS-consuming recipient mice. Results A randomized controlled interventional clinical trial The trial featured four NNS intervention arms: aspartame, saccharin, sucralose, and stevia. All NNS were given as commercially available sachets containing glucose as a bulking agent (2 sachets/3 times a day), corresponding to 8%, 20%, 34%, and 75% of the acceptable daily intake (ADI) of each of the supplemented NNS, respectively ( US Food and Drug Administration, 2018 * US Food and Drug Administration Additional information about high-intensity sweeteners permitted for use in food in the United States. Food Additives and Petitions. 2018; * Google Scholar ) (Figures 1A-1C, STAR Methods). To control for the potential confounding effect of the glucose vehicle, routinely incorporated into the sachet mixtures, we supplemented participants in a fifth arm with an equivalent amount of glucose (5 g day^-1). A sixth group did not receive any supplement (no supplement control, NSC). The study consisted of three phases: 7 days of baseline measurements of metabolic, metabolomic, and microbial parameters were followed by 14 days of exposure to the various nutritional interventions, after which supplementation was ceased and participants were followed up for 7 additional days. To determine the effect of NNS on glycemic control, participants wore a continuous glucose monitor (CGM) throughout the 29 days of the trial, and glucose tolerance tests were performed on pre-determined days. Anthropometrics and blood tests were conducted on days 0, 14, and 28. Microbiome samples from the stool and the oral cavity were collected at pre-determined time points. Participants logged all food intake and physical activity in real time using a dedicated smartphone application. The study design is summarized in Figure 1A. Figure 1Randomized-controlled trial to determine the effects of NNS on glycemic control and the microbiome Show full caption (A) Study design in humans. Data and sample collection days are denoted by black circles. (B) CONSORT flow diagram. Participants flow during the process of recruitment, randomization, follow-up, and data analysis in the study. (C) Doses and chemical structures of the NNS used in this study. ADI, acceptable daily intake; 60kg, human body weight. (D) Baseline glucose tolerance tests show high inter-personal and low intra-personal variability, related to Figure 2. Two oral glucose tolerance tests were performed with 50 g of glucose during a baseline week prior to any dietary intervention. Spearman correlation was calculated between the incremental area under the glucose curve for each individual. Colors correspond to the groups. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Between 2018 and 2020, we screened 1,375 individuals for eligibility (see exclusion criteria in STAR Methods). A unique feature of the trial consisted of inclusion only of participants defined as complete NNS abstainers according to a detailed food frequency questionnaire based on NNS-containing products on the Israeli market (STAR Methods ). Indeed, using the stringent screening protocol, the vast majority of ineligible candidates were found to consume NNS, in many cases unknowingly, in line with a similar finding in a US cohort ( Sylvetsky et al., 2017b * Sylvetsky A.C. * Walter P.J. * Garraffo H.M. * Robien K. * Rother K.I. Widespread sucralose exposure in a randomized clinical trial in healthy young adults. Am. J. Clin. Nutr. 2017; 105: 820-823 * Crossref * PubMed * Scopus (20) * Google Scholar ). A total of 120 participants, 20 in each group, successfully completed the trial and had enough glucose measurements for analysis (for the consolidated standards of reporting trials [CONSORT] participants flow diagram, see Figure 1B and STAR Methods). Table S1 shows the baseline characteristics of the cohort and each group. Participants in all groups were in good metabolic health, featuring normal body mass index (BMI), waist-hip ratio, hemoglobin A1c (HbA1c), C-reactive protein (CRP), total and high-density lipoprotein (HDL) cholesterol, blood pressure, heart rate, serum alanine transaminase (ALT), and aspartate transaminase (AST). 65% of participants were women, and the median age was 29.95 (interquartile range [IQR] 26.93-35.23). None of the following covariates was significant in any of the groups: age, sex, BMI, smoking, and habitual diet (Table S2). Saccharin and sucralose impair glucose tolerance in healthy adults As a standardized measure of glucose tolerance, all participants performed oral glucose tolerance tests (GTT) with 50 g of glucose consumed in the morning after an overnight fasting on pre-determined days, twice during weeks 1, 2, and 4, and 3 times on week 3 (a total of 9 GTTs). GTTs were performed by participants at home, and the CGM recorded interstitial glucose every 15 min, after which the incremental area under the glucose curve (iAUC) was calculated. Performing GTTs using continuous glucose monitoring at home (rather than inviting the participants to perform the GTT at the testing center) enabled to longitudinally assess the effect of acute NNS consumption and reduce noise ( Bailey et al., 2014 * Bailey T.S. * Ahmann A. * Brazg R. * Christiansen M. * Garg S. * Watkins E. * Welsh J.B. * Lee S.W. Accuracy and acceptability of the 6-day Enlite continuous subcutaneous glucose sensor. Diabetes Technol. Ther. 2014; 16: 277-283 * Crossref * PubMed * Scopus (65) * Google Scholar ). Although considerable person-to-person heterogeneity was observed in the GTT-iAUC (range 1,225-7,458 mg dL^-1 min^-1), baseline GTTs performed by the same individual were similar and significantly correlated with each other (Spearman r = 0.44, p < 0.0001, Figure 1 D), in line with previous findings ( Zeevi et al., 2015 * Zeevi D. * Korem T. * Zmora N. * Israeli D. * Rothschild D. * Weinberger A. * Ben-Yacov O. * Lador D. * Avnit-Sagi T. * Lotan-Pompan M. * et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015; 163: 1079-1094 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (1202) * Google Scholar ). The effects of the NNS and controls on glucose tolerance are summarized in Figure 2. To consider each individual GTT, we used two linear-mixed-effects models (LMMs) to assess the impact of NNS consumption over time, either with the seven GTTs performed during baseline and exposure weeks (model A) or all GTTs including follow-up (model B; Figure 2A). Both saccharin and sucralose significantly elevated glycemic response during exposure (model A) compared with glucose vehicle (p = 0.042 and 0.004, respectively) and NSC (p = 0.018 and 0.001; Figure 2A). The significant effect on GTT-iAUC was largely limited to the exposure period (model B saccharin versus glucose p = 0.18 and versus NSC p = 0.21; model B sucralose versus glucose p = 0.051 and versus NSC p = 0.059). Aspartame and stevia did not show a significant effect in both models. Importantly, no significant effect on glucose tolerance was observed in the glucose vehicle or NSC groups (Figure 2A; Table S2). To compare the magnitude of the effect between groups, we normalized the iAUC-GTT of each individual with their average baseline iAUC-GTT. During the 1st week of exposure, the normalized glycemic response was significantly higher in the sucralose group compared with the glucose vehicle ( Figure 2B, two-way ANOVA & Dunnett p = 0.037) and the NSC groups ( Figure 2B, p = 0.047) and in the saccharin group compared with the glucose vehicle group (Figure 2B, p = 0.023). The significantly elevated glycemic response persisted during the 2nd week of exposure compared with either glucose vehicle (Figure 2C, saccharin p = 0.047, sucralose p = 0.017) or NSC (Figure 2C, saccharin p = 0.003, sucralose p = 0.002) but declined toward baseline during follow-up ( Figure 2D). Importantly, despite considerable inter-individual heterogeneity of glycemic responses, there were no significant differences in GTT-iAUCs between the groups at baseline (one-way repeated-measures ANOVA F = 0.67, p = 0.64). As an additional output, we compared each individual with their own baseline (repeated-measures two-way ANOVA & Dunnett's). Saccharin significantly elevated glycemic response, starting from the 1st week of exposure (Figure 2E, p = 0.0073, iAUC mean difference 783.5, 95% confidence interval [CI] 204.3-1,363) and persisting to the 2nd week of exposure (p = 0.0094, mean 811.2, CI 190.6-1,432). These differences abated upon cessation of saccharin intake (p = 0.39). Sucralose supplementation likewise resulted in significantly elevated glycemic response during week 1 (Figure 2F, p = 0.018, mean 855.8, CI 134.8-1,577) and week 2 (p = 0.0092, mean 976.3, CI 231.3-1,721), which did not persist in the follow-up week (p = 0.34). In contrast, neither aspartame (Figure 2G) nor stevia (Figure 2H) had a significant effect on glucose tolerance during the 1st (p = 0.9 and 0.47, respectively) and 2nd (p = 0.99 and 0.78, respectively) exposure weeks, or during follow-up (p = 0.32 and 0.99, respectively). No significant effect on glucose tolerance was observed in the glucose vehicle group (Figure 2I, week 1, p = 0.62, week 2, p = 0.85, follow-up, p = 0.33) or the NSC group (Figure 2J, p = 0.77, 0.41, and 0.11). The lack of significant effect in these control groups indicates that reduced glucose tolerance in the saccharin and sucralose groups is not a result of daily supplementation with a glucose-containing mixture (as glucose quantity is comparable between the vehicle and NNS groups, STAR Methods) or the experimental protocol, which was identical in all groups (i.e., nine GTTs performed during the 29 days trial in all participants). Taken together, these findings indicate that short-term consumption of sucralose and saccharin in doses lower than the ADI can impact glycemic responses in healthy individuals. Figure 2Saccharin and sucralose detrimentally affect glycemic response in humans Show full caption Oral glucose tolerance tests were performed before, during, and after exposure to saccharin, sucralose, aspartame, stevia, and glucose vehicle in an amount equivalent to bulking glucose in the NNS, or no supplement control (NSC). The incremental area under the glucose curve (GTT-iAUC) was calculated. (A) Schematic and p values of two linear-mixed models comparing GTT-iAUCs of NNS and controls; model A included baseline and exposure GTTs, model B also included the follow-up GTTs. (B-D) GTT-iAUC of each individual were normalized to their average baseline GTT-iAUC and compared between the groups during the (B) 1st and (C) 2nd week of exposure, or the (D) follow-up week after cessation. Each dot represents an individual; horizontal lines, quartiles. (E-J) GTT-iAUCs of each individual in the (E) saccharin, (F) sucralose, (G) aspartame, (H) stevia, (I) glucose vehicle, or (J) NSC groups were averaged per-person during each week of the trial and compared with their baseline. Black line, connects the means; horizontal lines, median;whiskers, 10-90 percentiles. ^*p < 0.05; ^**p < 0.01; repeated-measures two-way ANOVA & Dunnett. Related to Figures S1 and S8. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) In addition to a standardized GTT, we sought to determine whether NNS supplementation also affected daily fluctuations in glucose levels as an additional clinically-relevant parameter of glucose homeostasis. The daily coefficient of variance (CoV) in glucose as derived from the CGM indicated higher variability in the saccharin (linear-mixed-effects regression calculated on all days p = 0.0003, Figure S1A) and stevia (p = 0.005, Figure S1B) groups, but not in the sucralose (Figure S1C) or aspartame (Figure S1D) groups, compared with NSC. However, this was likely due to lower variability in the NSC group (p = 0.07 versus glucose vehicle), and none of the NNS groups was significantly different than the glucose vehicle group ( Figures S1A-S1D). As both saccharin and sucralose demonstrated a cohort-wide effect on glucose tolerance, we asked whether the elevated glycemia associated with their intake is due to NNS effects on glucagon-like peptide-1 (GLP-1) or insulin production. Although participants in the NNS and the glucose vehicle groups were all exposed to an equivalent amount of glucose, a significant increase in plasma insulin during exposure was noted in the glucose vehicle group (mean 7.27 mU L^-1, 0.14-14.4, two-way ANOVA & Dunnett p = 0.045, Figure S1E), which remained elevated on the last day of the trial (10.27 mU L^-1, 3.4-17.1, p = 0.004, Figure S1E) and during exposure in the stevia group (6.38 mU L^-1, 0.19-12.58, p = 0.043, Figure S1 E). However, there were no significant changes in blood insulin in the saccharin, sucralose, aspartame, or NSC groups (Figure S1E). GLP-1 levels were not significantly altered in any of the groups ( Figure S1F). None of the measured anthropometrics (BMI, waist and hip circumference, systolic and diastolic blood pressure, and resting heart rate) or blood markers (blood pressure, HbA1c, C-reactive protein, ALT, AST, and blood immune cell counts, see STAR Methods for full list) were significantly impacted (following Benjamini-Hochberg correction for multiple hypothesis testing) by NNS supplementation compared with the control groups (Table S1). Figure S1Effect of saccharin and sucralose on additional measurements of glycemic control, related to Figure 2 Show full caption (A-D) Interstitial glucose levels were monitored continuously throughout the trial using a continuous glucose monitor (CGM). The coefficient of variance (CoV) was calculated as a measurement of blood glucose fluctuations, in the glucose vehicle and NSC groups compared with (A) saccharin, (B) stevia, (C) sucralose, or (D) aspartame. Symbols, mean; error bars, SEM; Significance according to linear mixed effects regression. (E and F) Blood samples were collected on the 1st and last day of the trial, and after 1 week of supplementation. Levels of non-fasting plasma (E) insulin and (F) glucagon-like peptide-1 (GLP-1) were measured using ELISA. ^* p < 0.05; ^** p < 0.01; two-way ANOVA and Dunnett. Horizontal lines, median; whiskers, 10-90 percentiles. NSC, no supplement control. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) To determine whether supplementation with sweeteners affected participant nutrient intake or physical activity, participants in the study logged their meals and activities in real time throughout the 4-week trial period. Although all participants modestly reduced their energy intake throughout the trial, regardless of the group (Table S3 ), likely due to the established effect of heightened awareness ( Robinson et al., 2015 * Robinson E. * Hardman C.A. * Halford J.C.G. * Jones A. Eating under observation: a systematic review and meta-analysis of the effect that heightened awareness of observation has on laboratory measured energy intake. Am. J. Clin. Nutr. 2015; 102: 324-337 * Crossref * PubMed * Scopus (0) * Google Scholar ), there were no significant differences in nutrient (carbohydrates, sugar, fiber, protein, fat, and cholesterol) intake or physical activity between the groups (Table S3). It is therefore unlikely that the differences in glycemic responses stem from differential intake of calories or macronutrients or physical activity. Non-nutritive sweeteners functionally modulate the human gut and oral microbiomes One of the mechanisms through which NNS can affect human metabolism may involve alteration of the intestinal microbiome. Evidence for this mechanism stems mostly from animal models, whereas evidence in humans is limited and conflicted ( Harrington et al., 2022 * Harrington V. * Lau L. * Crits-Christoph A. * Suez J. Interactions of non-nutritive artificial sweeteners with the microbiome in metabolic syndrome. Immunometabolism. 2022; 4: e220012 * Crossref * PubMed * Google Scholar ). Importantly, as previous studies utilized 16S rDNA microbiome profiling, the effect of NNS on species-level abundance and functional capacity of the microbial community remains elusive. We therefore collected longitudinal stool samples from all participants throughout the baseline, exposure, and follow-up phases and performed shotgun metagenomic sequencing (n = 1,182 stool samples after quality filtration, STAR Methods). Baseline stool microbiome composition and function were comparable between the NNS (aspartame, sucralose, saccharin, and stevia) and control groups (glucose vehicle, NSC, and PERMANOVA p > 0.05, Table S4). To determine whether NNS supplementation had an effect on the microbiome's temporal dynamics, we performed a trajectory analysis using M-product ( Kilmer et al., 2021 * Kilmer M.E. * Horesh L. * Avron H. * Newman E. Tensor-tensor algebra for optimal representation and compression of multiway data. Proc. Natl. Acad. Sci. USA. 2021; 118 (e2015851118) * Crossref * PubMed * Scopus (7) * Google Scholar ) based tensor component analysis (TCAM, Mor et al., 2022 * Mor U. * Cohen Y. * Valdes-Mas R. * Kviatcovsky D. * Elinav E. * Avrom H. Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations. PLoS Comput Biol. 2022; 18https://doi.org/10.1371/ journal.pcbi.1010212 * Crossref * PubMed * Scopus (0) * Google Scholar ; STAR Methods) compared with the NSC group. This analysis was performed twice, using samples from individual sampling days as well as weekly averaged abundances per participant. A significant effect on the microbiome composition was observed in the sucralose (genus days PERMANOVA, p = 0.002, Figure 3A; genus weeks, p = 0.011, Figure S2A; species days, p = 0.033, Figure S2B; species weeks, p = 0.012, Figure S2C) and saccharin (genus days, p = 0.014, Figure 3B; species days, p = 0.018, Figure S2D) groups. All four NNS had a significant effect on microbiome function (sucralose weeks, p = 0.033, Figure 3C; saccharin weeks, p = 0.023, Figure 3D; saccharin days, p = 0.04. Figure S2E; aspartame weeks, p = 0.014, Figure 3E; aspartame days, p = 0.016, Figure S2F; stevia weeks, p = 0.036, Figure 3F; stevia days, p = 0.017, Figure S2G), as well as KEGG modules in sucralose (days, p = 0.015, Figure S2H). None of these microbiome features was significantly different between the glucose vehicle and the NSC groups (Figures S2I-S2O). Figure 3NNS functionally modulate the gut microbiome Show full caption Stool microbiome samples were collected at pre-determined days and analyzed for bacterial composition (using Kraken2 & Bracken) and function (MetaCyc, as well as KEGG modules and pathways). (A-F) Trajectory analysis ordination plots following tensor component analysis with M-product (TCAM), which tested if a group had a significant trajectory compared with the no supplement control group. TCAM was applied to the fold change from subject's baseline for each feature. This analysis was performed using samples from individual days as well as weekly averaged abundances per participant (indicated in panels). (A) Bacterial genera in the sucralose group. (B) Bacterial genera in the saccharin group. (C-F) MetaCyc pathways in the (C) sucralose, (D) saccharin, (E) aspartame, or (F) stevia group. (G-J) Top loadings in each MetaCyc comparison in the (G) sucralose, (H) saccharin, (I) aspartame, or (J) stevia groups. (A-F) Hypothesis testing for trajectory analysis according to the PERMANOVA test. (G-J) Bars, mean; error bars, SEM. NSC, no supplement control; Log. log2. Related to Figures S2 and S3. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Figure S2Effect of NNS and controls on the microbiome, related to Figure 3 Show full caption Stool microbiome samples were collected at pre-determined days and analyzed for composition and function. (A-H) Trajectory analysis ordination plots following tensor component analysis using M product (TCAM), which tested if a group had a significant trajectory compared with the no supplement control group. TCAM was applied to the fold change from baseline for each feature. (A) Bacterial genera in the sucralose group. (B and C) Bacterial species in the sucralose group stratified by (B) days or (C) weeks. (D) Bacterial species in the saccharin group. (E-G) MetaCyc pathways in the (E) saccharin, (F) aspartame, or (G) stevia group. (H) KEGG modules in the sucralose group. (I-O) Comparison of the glucose vehicle group to the no supplement control: (I and J) genus, (K and L) species, (M and N) MetaCyc pathways, or (O) KEGG modules. Significance of the trajectory analysis according to PERMANOVA. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) To determine the microbial features underlying these effects, we plotted the area under the log-fold change curve for the top loadings of each significant comparison. Several top loadings in the sucralose group were related to purine metabolism (Figure 3G). Top loadings for the saccharin group included pathways related to glycolysis and glucose degradation (Figure 3H). Many of the top loadings in the aspartame group were related to polyamines metabolism (Figure 3I). Several top loadings in the stevia group were related to fatty acid biosynthesis (Figure 3J). Collectively, these results suggest that dietary supplementation with NNS can impact the functional potential of the human microbiome in NNS-specific manners, with the most prominent effects on the fecal microbiome observed with sucralose. Similarly, NNS distinctly impacted the oral microbiome (eight samples per participant throughout the trial, STAR Methods). The relative abundance of four metabolism-related KEGG pathways (Figures S3A-S3D) and three modules (Table S5) decreased during the 2nd week of exposure in the oral microbiome of the stevia group. Notable oral microbiome alterations (p < 0.1) in the other NNS groups include changes in relative abundances of six Streptococcus species in the sucralose group (Figure S3E; Table S6), reduced relative abundance of Fusobacterium in the saccharin group (Figure S3F; Table S5), and reduced abundance of Porphyromonas (Figure S3G) and Prevotella nanceiensis (Figure S3H) in the aspartame group (Table S5). There were no oral microbiome alterations with an FDR-corrected p < 0.1 in either control group (Table S6), and baseline oral microbiome composition and function were comparable between the NNS and control groups (PERMANOVA p > 0.05, Table S4). Figure S3NNS modulate the oral microbiome composition and function, related to Figure 3 Show full caption Relative abundances of features altered during exposure to NNS compared with baseline (FDR-corrected Friedman p < 0.1). (A-D) KEGG pathways in the stevia group. (E-H) (E) Streptococcus species in the sucralose group (F) Fusobacterium, saccharin, (G) Porphyromonas, aspartame, and (H) Prevotella nanceiensis, aspartame. ^* p < 0.05; ^** p < 0.01; two-way ANOVA and Dunnett. Horizontal lines, median; whiskers, 10-90 percentiles. NSC, no supplement control. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Microbiome features correlate with NNS impacts on the human glycemic response We next sought to identify microbiome features that potentially contribute to the NNS effects noted on glycemic control and uncover putative host targets that may link these NNS-related functional microbiome alterations to an effect on the host. We initially focused on sucralose as this NNS is poorly absorbed and is more likely to interact with the intestinal microbiome. Furthermore, of the two NNS that significantly impacted glucose tolerance, the sucralose group displayed greater person-to-person heterogeneity, providing an opportunity to elucidate microbiome and metabolome contributions to personalized glycemic responses. First, we correlated the baseline abundance of stool bacterial genera and species, KEGG modules and pathways, and MetaCyc pathways with the GTT-iAUC measured during the 2nd week of sucralose exposure. We then assessed how the significantly correlated features (Pearson p < 0.05) changed throughout the trial (Table S6). Significant metagenomic results included those strictly appearing in the sucralose group (n = 20), although not being altered in the glucose (n = 20) and NSC (n = 20) control groups. The plasma metabolome for sucralose consumers was profiled at baseline (day 0) and after the 1st week of NNS supplementation (day 14). Significant metabolomic results included those strictly appearing in the sucralose group (n = 20), although not being altered in the glucose (n = 10) and NSC (n = 10) control groups (Table S6). Baseline abundance of three bacterial species correlated with GTT-iAUC (Figure 4A). Functionally, the abundances of several purine biosynthesis pathways were positively associated with GTT-iAUC and gradually decreased during the trial. Mixed acid fermentation and the TCA cycle were also inversely correlated with GTT-iAUC; the abundances of these pathways increased during both exposure weeks and trended toward baseline during follow-up (Figure 4A). Nine metabolites significantly increased in plasma during sucralose supplementation and three decreased (FDR-corrected paired t test p < 0.05, Figures 4B-4D). No significant changes were noted following FDR correction in plasma metabolites in the glucose vehicle and NSC groups (Table S6). In line with the increased abundance of the TCA cycle pathway in the microbiome, the levels of iso-citrate and trans-aconitate, TCA cycle intermediates, increased in plasma during sucralose supplementation (Figure 4B). Levels of the amino acids serine, N-acetyl alanine, and aspartate, as well as the aspartate metabolite quinolinate, also increased during supplementation ( Figure 4B). Two additional TCA cycle metabolites (citrate and fumarate) and several additional amino acids (cystine, lysine, and glycyl-L-valine) significantly increased during supplementation (before FDR correction, Table S6). In line with the reduction of microbial pathways related to purine metabolism, plasma levels of pseudouridine and uric acid were significantly reduced during sucralose supplementation (Figure 4D), as well as guanosine, 1-methylguanine, inosine, and paraxanthine (before FDR correction, Table S5). Figure 4Microbial features and plasma metabolites are correlated with sucralose's effect on glycemic responses Show full caption (A) Per week abundance of bacterial species and KEGG and MetaCyc pathways significantly (Pearson p < 0.05) correlated at baseline with baseline-normalized per-person glycemic response in the 2nd week of exposure. (B-D) Plasma metabolites significantly (FDR-corrected Student's t test p < 0.05) increased compared with baseline after the 1st week of exposure. (E) Pathway enrichment based on metabolites in (B-D) and metabolites significantly correlated with GTT-iAUC. Pathways in bold are significant (p < 0.05) after FDR correction. (F) Metabolites significantly different between the top and bottom five responders based on two-way ANOVA. Metabolites in bold are significant after FDR correction. ^**p < 0.01; ^***p < 0.001; ^****p < 0.0001, FDR-corrected Student's t test. Horizontal lines, median; whiskers, 10-90 percentiles. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) In addition, 22 metabolites were significantly correlated with an increase in GTT-iAUC noted in the sucralose group, but not in the NSC or glucose vehicle group, including the SCFA propionate, butyrate, and valerate (before FDR correction, Table S6). Based on all of the above differentially abundant metabolites, we performed a pathway enrichment analysis which highlighted changes in amino acid metabolism and biosynthesis and the TCA cycle pathways to be associated with the impact of sucralose on glycemic control (FDR-corrected p < 0.05, Figure 4E). To identify metabolites that may mediate responsiveness to sucralose and those that are potentially involved in sucralose effect on glucose tolerance, we next compared the metabolomic profiles of the top and bottom five sucralose responders. Three metabolites were significantly different between top and bottom responders (FDR-corrected two-way ANOVA p < 0.05, Figure 4F): the ketone body beta-hydroxybutyrate, serine, and the cysteine derivate cysteate. All three were lowest in top sucralose responders at baseline and increased during supplementation. None of these metabolites was significantly different between the five top and bottom responders in the glucose and NSC control groups (Table S6 ). We similarly correlated the abundances of baseline metagenomic features with GTT-iAUC in the saccharin, stevia, and aspartame groups, as well as metabolomic profiles in the top 5 responders in each group. Significant metagenomic results included those strictly appearing in any of the saccharin, aspartame or stevia groups, although not being altered in the glucose and NSC control groups. The plasma metabolome for sucralose consumers was profiled at baseline (day 0) , after the 1st week of NNS supplementation (day 14) and at end of the follow-up (day 28). Significant metabolomic results included those strictly appearing in the any of the saccharin, aspartame, or stevia groups, although not being altered in the respective glucose and NSC control groups. In the saccharin group, baseline levels of Prevotella copri and UMP biosynthesis were positively associated with GTT-iAUC and gradually increased during exposure, whereas baseline levels of Bacteroides xylanisolvens were negatively associated with GTT-iAUC and increased during exposure ( Figure 5A). Many of the pathways negatively correlated with GTT-iAUC were related to glycolysis and glycan degradation (Figure 5A). Untargeted plasma metabolomics of the top five saccharin responders readily detected high levels of saccharin during exposure (Figure 5 B). The plasma levels of indoxyl sulfate, a metabolite associated with vascular disease, increased during saccharin exposure (Figure 5 B). Levels of the SCFA butyrate increased during the trial, whereas those of three long-chain fatty acids were reduced (Figure 5B; Table S5). In the stevia group, two Prevotella spp. were positively associated with GTT-iAUC, and reduced during exposure; Bacteroides coprophilus, Parabacteroides goldsteinii, and a Lachnospira spp., which were also positively associated with GTT-iAUC increased during both exposure weeks (Figure 5C). Stevioside was readily detected in plasma samples of the stevia group, exclusively during supplementation (Figure 5D). Levels of the amino acids serine and lysine increased during stevia supplementation (Figure 5D). Notably, two metabolites of arginine, ornithine and citrulline, also increased during exposure (Figure 5D). In the aspartame group, B. fragilis and B. acidifaciens were positively associated with GTT-iAUC, whereas B. coprocola had an inverse correlation (Figure 5E). Levels of kynurenine, a metabolite associated with diabetes, increased during aspartame consumption (Figure 5F). Collectively, human NNS supplementation induced distinct alterations in microbiome composition and function, as well as in distinct plasma metabolites, in each of the NNS-supplemented groups, which correlated with host glycemic responses. Notably, changes in abundance of many of the correlated microbiome or metabolome features start as early as the 1st week of exposure and revert to baseline during follow-up, suggesting that these bacterial species and functions may respond to the presence of NNS. Figure 5Effects of saccharin, stevia, and aspartame on the microbiome correlate with glycemic response Show full caption (A, C, and E) Per week abundance of bacterial species and KEGG and MetaCyc pathways significantly (Pearson p < 0.05) correlated at baseline with baseline-normalized per-person glycemic response in the 2nd week of exposure, in the (A) saccharin, (C) stevia, or (E) aspartame group. (B, D, and F) Plasma metabolites significantly (non-FDR-corrected one-way ANOVA & Dunnet p < 0.05) increased compared with baseline after the 1st week of exposure, in the (B) saccharin, (D) stevia, or (F) aspartame groups. ^*p < 0.05; ^**p < 0.01; ^***p < 0.001. Horizontal lines, median; whiskers, 10-90 percentiles. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Causative personalized impacts of NNS-modulated microbiome on glycemic responses To determine whether the aforementioned alterations in human microbiome configuration causally contribute to NNS-induced hyperglycemia, we colonized adult GF mice with stool microbiome collected either at the beginning of the trial (day 1), or on the last day of exposure (day 21), from all four NNS-supplemented and control groups (Figure S4A). In total, we transplanted microbiomes from 42 individuals, the four individuals in each group that had the most potent response ("top responders"), and the three that had the lowest response ("bottom responders") in their respective groups. Statistically, each individual donor was strictly treated as a random effect in the LMM while modeling the glycemic response variable. Strikingly, for each of the four "top responders" in the saccharin and sucralose groups, mice humanized with the day 21 sample had a significantly higher glycemic response compared with mice that received the baseline sample from the same individual (saccharin top 1-t test p = 0.0072, top 2 p = 0.025, top 3 p = 0.0046, top 4 p = 0.048, Figure S4; sucralose top 1 p = 0.025, top 2 p = 0.016, top 3 p = 0.043, top 4 p = 0.0031, Figure S5), resulting in a significant group effect for both saccharin (Figure 6A, mixed-effects ANOVA p < 0.0001) and sucralose (Figure 6B, p < 0.0001). Microbiomes transferred from the last day of exposure of the four "top responders" to stevia and aspartame resulted in elevated glycemic response in recipient mice of three out of the four top-responding donors (stevia p < 0.0001, p < 0.0001, p = 0.029; aspartame p = 0.012, 0.048, and 0.015, Figure S6), resulting in a significant group effect for both (stevia p < 0.0001, Figure 6C; aspartame p = 0.0022, Figure 6D). Importantly, none of the day 21 stool microbiome samples transferred into GF mice from the four "top responders" in the glucose vehicle or the NSC groups (Figures 6E and 6F; Figure S7) resulted in a significant effect on glucose tolerance, compared with baseline microbiome samples transferred from the same individuals. To further elucidate the extent of the causative personalized NNS-mediated effect size, we transplanted baseline and day 21 microbiomes from the bottom three responders in each group into GF mice. Interestingly, in the saccharin group, the last day of exposure microbiomes from the bottom three responders still elevated glycemic response in recipient GF mice (Figure 6G, p = 0.0003), in line with its strong cohort-wide elevated glycemia. No significant effects on glycemic response were observed with bottom responders from any of the other treatment (sucralose p = 0.23, Figure 6H; stevia p = 0.96, Figure 6I; aspartame p = 0.076, Figure 6J) or control (glucose vehicle p = 0.97, Figure 6K; NSC p = 0.058, Figure 6L) groups, suggesting a personalized microbiome-mediated impact in response to NNS exposure in these groups. Collectively, the glycemic responses in the humanized mice largely reflected those of their NNS-supplemented donors and serve as a likely causal link between NNS-related microbiome modulations and disrupted glycemic control. Figure S4Microbiomes of saccharin consumers causally linked to elevated glycemic response in germ-free mice, related to Figure 6 Show full caption (A) Experimental design in conventionalized GF mice, related to Figure 6. The top four and bottom three responders in each of the six groups were defined following normalization to baseline of GTT-iAUCs during the 2nd week of exposure. From each of these 42 individuals, fecal samples from baseline and the last day of exposure (day 21) were used to conventionalize GF mice. A GTT was performed on recipient mice 7 days post colonization. (B-H) Groups of age-matched germ-free male Swiss-Webster mice were transplanted with stool microbiomes taken during baseline and the last day of exposure to saccharin from the (B-E) top and (F-H) bottom glycemic responders (Figure 2). A glucose tolerance test was performed 6 days post-transplant (plotted with AUC). (B) Recipients of top 1 microbiome: baseline N = 9, day 21 N = 9. (C) Recipients of top 2 microbiome: baseline N = 7, day 21 N = 7. (D) Recipients of top 3 microbiome: baseline N = 7, day 21 N = 7. (E) Recipients of top 4 microbiome: baseline N = 8, day 21 N = 9. (F) Recipients of bottom 1 microbiome: baseline N = 8, day 21 N = 8. (G) Recipients of bottom 2 microbiome: baseline N = 9, day 21 N = 9. (H) Recipients of bottom 3 microbiome: baseline N = 9, day 21 N = 6. ^*p < 0.05; ^**p < 0.01; two-way ANOVA and Dunnett (in GTT panels) or Student's t test (in AUC panels). Lines (AUC) and symbols (GTT), mean; error bars, SEM. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Figure S5Microbiomes of sucralose responders causally linked to elevated glycemic response in germ-free mice, related to Figure 6 Show full caption (A-G) Groups of age-matched germ-free male Swiss-Webster mice were transplanted with stool microbiomes taken during baseline and the last day of exposure to sucralose from the (A-D) top and (E-G) bottom glycemic responders (Figure 2). A glucose tolerance test was performed 6 days post-transplant (plotted with AUC). (A) Recipients of top 1 microbiome: baseline N = 5, day 21 N = 7. (B) Recipients of top 2 microbiome: baseline N = 6, day 21 N = 6. (C) Recipients of top 3 microbiome: baseline N = 10, day 21 N = 9. (D) Recipients of top 4 microbiome: baseline N = 6, day 21 N = 7. (E) Recipients of bottom 1 microbiome: baseline N = 8, day 21 N = 10. (F) Recipients of bottom 2 microbiome: baseline N = 8, day 21 N = 8. (G) Recipients of bottom 3 microbiome: baseline N = 9, day 21 N = 8. ^* p < 0.05; ^** p < 0.01; Student's t test. Lines (AUC) and symbols (GTT), mean; error bars, SEM. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Figure 6NNS-associated alteration in human microbiome causally linked to hyperglycemia Show full caption 7- to 9-week-old male Swiss-Webster germ-free mice were conventionalized by oral gavage with microbiome extracted from stool samples of the seven individuals that experienced the strongest effect on glucose tolerance (A-F, top four and G-L, bottom three) from each of the following groups: (A and G) saccharin, (B and H) sucralose, (C and I) stevia, (D and J) aspartame, (E and K) glucose vehicle, and (F and L) no supplement control. Magnitude of response is defined as the change in GTT-iAUC on the 2nd week of exposure compared with baseline. A glucose tolerance test was performed 7 days post-conventionalization. Experiments were conducted in pairs, in which two groups of mice received either the baseline sample or the last day of exposure (day 21) sample. (A) Recipients of saccharin top responders' microbiomes: top 1 baseline N = 9, day 21 N = 9; top 2 N = 7 and 7; top 3 N = 7 and 7; top 4 N = 8 and 9. (B) Recipients of sucralose top responders' microbiomes: top 1 N = 5 and 7; top 2 N = 6 and 6; top 3 N = 10 and 9; top 4 N = 6 and 7. (C) Recipients of stevia top responders' microbiomes: top 1 N = 7 and 8; top 2 N = 6 and 9; top 3 N = 7 and 8; top 4 N = 9 and 8. (D) Recipients of aspartame top responders' microbiomes: top 1 N = 9 and 8; top 2 N = 5 and 3; top 3 N = 8 and 8; top 4 N = 8 and 6. (E) Recipients of glucose vehicle top responders' microbiomes: top 1 N = 8 and 7; top 2 N = 8 and 9; top 3 N = 6 and 6; top 4 N = 7 and 6. (F) Recipients of NSC top responders' microbiomes: top 1 N = 8 and 8; top 2 N = 6 and 6; top 3 N = 6 and 7; top 4 N = 6 and 6. (G) Recipients of saccharin bottom responders' microbiomes: bottom 1 N = 8 and 8; bottom 2 N = 9 and 9; bottom 3 N = 9 and 6. (H) Recipients of sucralose bottom responders' microbiomes: bottom 1 N = 8 and 10; bottom 2 N = 8 and 8; and bottom 3 N = 9 and 8. (I) Recipients of stevia bottom responders' microbiomes: bottom 1 N = 7 and 7; bottom 2 N = 9 and 9; and bottom 3 N = 6 and 7. (J) Recipients of aspartame bottom responders' microbiomes: bottom 1 N = 6 and 6; bottom 2 N = 7 and 5; and bottom 3 N = 12 and 10. (K) Recipients of glucose vehicle bottom responders' microbiomes: bottom 1 N = 7 and 6; bottom 2 N = 6 and 6; and bottom 3 N = 6 and 6. (L) Recipients of NSC bottom responders' microbiomes: bottom 1 N = 6 and 7; bottom 2 N = 9 and 9; and bottom 3 N = 6 and 6. The average of all donors is presented in the GTT panels. AUC insets - colors, respective donors in each group; GTT panels - symbols, mean; error bars, SEM. In AUC insets, lines, mean. ^** p < 0.01; ^ **** p < 0.0001, mixed-effects ANOVA with donor as random effect. Related to Figures S4, S5, S6, and S7. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Figure S6Microbiomes of stevia and aspartame responders causally linked to elevated glycemic response in germ-free mice, related to Figure 6 Show full caption (A-N) Groups of age-matched germ-free male Swiss-Webster mice were transplanted with stool microbiomes taken during baseline and the last day of exposure to (A-G) stevia or (H-N) aspartame from the (A-D and H-K) top and (E-G and L-N) bottom glycemic responders (Figure 2). A glucose tolerance test was performed 6 days post-transplant (plotted with AUC). (A) Recipients of stevia top 1 microbiome: baseline N = 7, day 21 N = 8. (B) Recipients of stevia top 2 microbiome: baseline N = 6, day 21 N = 9. (C) Recipients of stevia top 3 microbiome: baseline N = 7, day 21 N = 8. (D) Recipients of stevia top 4 microbiome: baseline N = 9, day 21 N = 8. (E) Recipients of stevia bottom 1 microbiome: baseline N = 7, day 21 N = 7. (F) Recipients of stevia bottom 2 microbiome: baseline N = 9, day 21 N = 9. (G) Recipients of stevia bottom 3 microbiome: baseline N = 6, day 21 N = 7. (H) Recipients of aspartame top 1 microbiome: baseline N = 9, day 21 N = 8. (I) Recipients of aspartame top 2 microbiome: baseline N = 5, day 21 N = 3. (J) Recipients of aspartame top 3 microbiome: baseline N = 8, day 21 N = 8. (K) Recipients of aspartame top 4 microbiome: baseline N = 8, day 21 N = 6. (L) Recipients of aspartame bottom 1 microbiome: baseline N = 6, day 21 N = 6. (M) Recipients of aspartame bottom 2 microbiome: baseline N = 7, day 21 N = 5. (N) Recipients of aspartame bottom 3 microbiome: baseline N = 12, day 21 N = 10. ^*p < 0.05; ^**p < 0.01;***p < 0.001; two-way ANOVA and Dunnett (in GTT panels) or Student's t test (in AUC panels). Lines (AUC) and symbols (GTT), mean; error bars, SEM. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Figure S7Microbiomes of the glucose vehicle or no supplement control groups do not elevate glycemic response in germ-free mice, related to Figure 6 Show full caption (A-N) Groups of age-matched germ-free male Swiss-Webster mice were transplanted with stool microbiomes taken during baseline and the last day of exposure to (A-G) glucose vehicle or (H-N) NSC from the (A-D and H-K) top and (E-G and L-N) bottom glycemic responders ( Figure 2). A glucose tolerance test was performed 6 days post-transplant (plotted with AUC). (A) Recipients of glucose vehicle top 1 microbiome: baseline N = 8, day 21 N = 7. (B) Recipients of glucose vehicle top 2 microbiome: baseline N = 8, day 21 N = 9. (C) Recipients of glucose vehicle top 3 microbiome: baseline N = 6, day 21 N = 6. (D) Recipients of glucose vehicle top 4 microbiome: baseline N = 7, day 21 N = 6. (E) Recipients of glucose vehicle bottom 1 microbiome: baseline N = 7, day 21 N = 6. (F) Recipients of glucose vehicle bottom 2 microbiome: baseline N = 6, day 21 N = 6. (G) Recipients of glucose vehicle bottom 3 microbiome: baseline N = 6, day 21 N = 6. (H) Recipients of NSC top 1 microbiome: baseline N = 8, day 21 N = 8. (I) Recipients of NSC top 2 microbiome: baseline N = 6, day 21 N = 6. (J) Recipients of NSC top 3 microbiome: baseline N = 6, day 21 N = 7. (K) Recipients of NSC top 4 microbiome: baseline N = 6, day 21 N = 6. (L) Recipients of NSC bottom 1 microbiome: baseline N = 6, day 21 N = 7. (M) Recipients of NSC bottom 2 microbiome: baseline N = 9, day 21 N = 9. (N) Recipients of NSC bottom 3 microbiome: baseline N = 6, day 21 N = 6. ^*p < 0.05; two-way ANOVA and Dunnett (in GTT panels) or Student's t test (in AUC panels). Lines (AUC) and symbols (GTT), mean; error bars, SEM. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Microbiome features correlate with person-specific variations in glycemic responses to NNS Finally, we compared fecal microbiome features potentially differentiating human top NNS responders (n = 4 in each NNS) and bottom responders (n = 3 in each NNS). In human participants, KEGG pathways Bray-Curtis-based dissimilarity to baseline configurations trended to be higher in top responders throughout the trial in the sucralose (Figure S8A), stevia (Figure S8B), and aspartame (Figure S8 C) groups and were initially higher in bottom responders but converged in the saccharin group (Figure S8D). Importantly, top and bottom "responders" were comparable in the glucose vehicle and NSC groups (Figures S8E and S8F). The fold change between baseline and the 2nd week of exposure of pathways related to glycolysis and TCA cycle was higher in sucralose top responders compared with bottom responders (Figure S8G). Pathways related to biosynthesis, degradation and metabolism of purines and pyrimidines increased in top stevia responders (Figure S8H). Pathways related to the urea cycle and its metabolites increased in top aspartame responders, whereas Akkermansia muciniphila, associated with metabolic health of the host ( Cani et al., 2022 * Cani P.D. * Depommier C. * Derrien M. * Everard A. * de Vos W.M. Akkermansia muciniphila: paradigm for next-generation beneficial microorganisms. Nature Reviews Gastroenterology & Hepatology. 2022; : 1-13 * PubMed * Google Scholar ), increased in bottom responders (Figure S8I). Degradation of the cyclic amide caprolactam increased in saccharin top responders, suggesting a possible potential for degradation of chemically related saccharin. Biosynthesis of the branched-chain amino acid isoleucine, associated with poorer metabolic health ( Yu et al., 2021 * Yu D. * Richardson N.E. * Green C.L. * Spicer A.B. * Murphy M.E. * Flores V. * Jang C. * Kasza I. * Nikodemova M. * Wakai M.H. * Tomasiewicz J.L. The adverse metabolic effects of branched-chain amino acids are mediated by isoleucine and valine. Cell metabolism. 2021; 33: 905-922 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (0) * Google Scholar ), also increased in top saccharin responders (Figure S8J). Figure S8Microbiome dissimilarities between NNS top and bottom glycemic responders, related to Figure 2 Show full caption The microbiome profile of the top four glycemic responders in each group was compared to its bottom three. Bray-Curtis dissimilarities were computed between all of the samples of a participant to their baseline samples, before averaging the values within a given week. (A-F) Bray-Curtis dissimilarity to baseline (2-3 samples per participant) based on KEGG pathways in the (A) sucralose, (B) stevia, (C) aspartame, (D) saccharin, (E) glucose vehicle, and (F) no supplement control groups. (G-J) Features whose fold change between baseline to 2nd week of exposure is highly variable between top and bottom responders in the (G) sucralose, (H) stevia, (I) aspartame, or (J) saccharin groups. ^*p < 0.05; Student's t test. Symbols, mean; error bars, SEM. NSC, no supplement control. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) To further exemplify such personalized microbiome differences, we compared the fecal microbiome configurations of GF mouse groups transplanted with microbiomes of top (n = 3) and bottom (n = 3) human sucralose responders (n = 3, Figure 7A), in which each of the top human donors and none of the bottom donors exhibited a significant difference in glycemic response between baseline and day 21 of the clinical trial (Figure S5). GF mouse recipients of microbiomes from top sucralose human responders on day 21 featured a distinct microbiome configuration compared with GF mouse recipients of microbiome from bottom sucralose human responders on day 21 (Rcpt: D21, PERMANOVA with donor as random effect PC3 p = 0.008, Figure 7B), in line with the differential glycemic responses noted in the human donors at day 21 time point (Figure S5). Total 22 species and 19 pathways were differentially abundant between mouse recipients of microbiomes from top and bottom human sucralose responders on day 21, of which only two were also different at baseline (pyrimidine biosynthesis and tRNA charging, Table S7). The putrescine biosynthetic pathway was overrepresented in mice conventionalized with top responders' day 21 samples (p < 0.0001); elevated plasma levels of putrescine were previously associated with type-2 diabetes ( Fernandez-Garcia et al., 2019 * Fernandez-Garcia J.C. * Delpino-Rius A. * Samarra I. * Castellano-Castillo D. * Munoz-Garach A. * Bernal-Lopez M.R. * Queipo-Ortuno M.I. * Cardona F. * Ramos-Molina B. * Tinahones F.J. Type 2 diabetes is associated with a different pattern of serum polyamines: a Case^-Control study from the PREDIMED-Plus trial. J. Clin. Med. 2019; 8: 71 * Crossref * PubMed * Google Scholar ) and gestational diabetes ( Liu et al., 2021 * Liu C. * Wang Y. * Zheng W. * Wang J. * Zhang Y. * Song W. * Wang A. * Ma X. * Li G. Putrescine as a novel biomarker of maternal serum in first trimester for the prediction of gestational diabetes mellitus: A nested case-control study. Front. Endocrinol. (Lausanne). 2021; 12: 759893https://doi.org/ 10.3389/fendo.2021.759893 * Crossref * PubMed * Scopus (0) * Google Scholar ). Figure 7Microbial functions linked to sucralose responsiveness and its effect on glucose tolerance Show full caption Fecal samples were collected from 7- to 9-week-old male Swiss-Webster germ-free mice 7 days post-conventionalization with microbiome extracted from stool samples of the three top and three bottom responders to sucralose. Magnitude of response is defined as the change in GTT-iAUC on the 2nd week of exposure compared with baseline. (A) Schematic design of microbiome analyses in recipient GF mice. (B) Principal components analysis of species-level (Bracken) composition in mice receiving day 21 samples from top and bottom responders. Horizontal lines, median; whiskers, 10-90 percentiles. ^ ** p < 0.01, PERMANOVA. (C) Replot for comparison of GTT (AUC in inset) of mice receiving baseline microbiome samples from top and bottom responders. GTT panels - symbols, mean; error bars, SEM. AUC insets - lines, mean. (D) Principal component analysis of species-level (Bracken) composition in mice receiving baseline samples from top and bottom responders. Horizontal lines, median; whiskers, 10-90 percentiles. ^ ** p < 0.01, PERMANOVA. (E) Same as (D) but based on MetaCyc pathways. (F) Pathways significantly (FDR-corrected linear-mixed model p < 0.05) differentially abundant (log[2] fold difference > 1) between mice receiving top and bottom responder baseline samples. PG, phosphatidylglycerol; Sat, saturated. (G) Spearman correlation of glycolysis pathway baseline abundance with fold difference in GTT-AUC of each of the conventionalized mouse groups. Circles represent the mean of the pathway abundance in all mice receiving a transplant from each donor, error bars, SEM. (H) Alpha diversity (observed species) in mice receiving baseline and day 21 samples from top and bottom responders. Significance according to linear-mixed effect regression. Error bars, SEM. (I) Same as (F), but top responders are compared on baseline and day 21; no pathways were overrepresented on day 21. (J) Spearman correlation of sucrose degradation pathway fold change abundance (day 21/baseline) with fold difference in GTT-AUC of each of the conventionalized mouse groups. (K) Same as (J) but glycogen degradation. Rcpt, recipient; BL, baseline; D21, day 21. * View Large Image * Figure Viewer * Download Hi-res image * Download (PPT) Notably, transplantation of baseline microbiomes of top and bottom sucralose responders into GF mice resulted in comparable glucose tolerance in recipient mice (Rcpt: BL, Figure 7C). Nonetheless, microbiome configurations of mice receiving baseline pre-exposure microbiomes from human top sucralose responders were already significantly different from those of recipients of baseline microbiomes from human bottom sucralose responders. Microbiome composition of mouse recipients of baseline samples of top and bottom human responders was significantly separated on PC3 (PERMANOVA with donor as random effect p = 0.008, Figure 7D), and function was separated on PC5 (p = 0.008, Figure 7E). The microbial features that potentially mediate these differences included 25 species and 27 pathways that are significantly (FDR-corrected linear-mixed model p < 0.05) differentially abundant between the two subsets (Table S7). Pathways related to menaquinol biosynthesis, previously associated with type 1 ( Roth-Schulze et al., 2021 * Roth-Schulze A.J. * Penno M.A.S. * Ngui K.M. * Oakey H. * Bandala-Sanchez E. * Smith A.D. * Allnutt T.R. * Thomson R.L. * Vuillermin P.J. * Craig M.E. * et al. Type 1 diabetes in pregnancy is associated with distinct changes in the composition and function of the gut microbiome. Microbiome. 2021; 9: 167 * Crossref * PubMed * Scopus (7) * Google Scholar ) and type 2 diabetes ( Balvers et al., 2021 * Balvers M. * Deschasaux M. * van den Born B.J. * Zwinderman K. * Nieuwdorp M. * Levin E. Analyzing type 2 diabetes associations with the gut microbiome in individuals from two ethnic backgrounds living in the same geographic area. Nutrients. 2021; 13: 3289 * Crossref * PubMed * Scopus (2) * Google Scholar ; Dash, and Al Bataineh, 2021 * Dash N.R. * Al Bataineh M.T. Metagenomic analysis of the gut microbiome reveals enrichment of menaquinones (vitamin K2) pathway in diabetes mellitus. Diabetes Metab. J. 2021; 45: 77-85 * Crossref * PubMed * Scopus (8) * Google Scholar ; Wu et al., 2020 * Wu H. * Tremaroli V. * Schmidt C. * Lundqvist A. * Olsson L.M. * Kramer M. * Gummesson A. * Perkins R. * Bergstrom G. * Backhed F. The gut microbiota in prediabetes and diabetes: a population-based cross-sectional study. Cell Metab. 2020; 32: 379-390.e3 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (80) * Google Scholar ), were the most overrepresented features in mouse recipients of baseline microbiomes from top responders (p = 0.0006, Figure 7F). Pathways related to purine (p < 0.0001) and pyrimidine (p = 0.005) biosynthesis were more abundant in mouse recipients of baseline microbiomes of top responders, whereas purine degradation (p < 0.0001) was more abundant in recipients of baseline microbiomes from bottom responders (Figure 7F), in line with the correlation between purine biosynthesis and elevated glycemic response noted in the entire sucralose group (Figure 4A). Most pathways over-abundant in mouse recipients of baseline microbiomes from bottom responders were related to fatty acid biosynthesis (stearate biosynthesis p = 0.014, dodecanoate biosynthesis p < 0.0001, saturated fatty acid elongation p = 0.005, and phosphatidylglycerol biosynthesis p = 0.003) and their utilization for energy production and gluconeogenesis (glyoxylate bypass and TCA p < 0.0001) (Figure 7F). To identify microbial pathways that may predict the effect of sucralose on glucose tolerance, we correlated the baseline abundance of each pathway with the fold difference in the GTT AUC of recipient mice (day 21/baseline). Interestingly, the baseline abundance of the glycolysis pathway demonstrated a significant (FDR-corrected) and strong correlation with GTT-AUC (Spearman r = 1, p = 0.0028, Figure 7 G; Table S7). Collectively, these results suggest that a unique pre-supplementation baseline microbiome configuration may contribute to the personalized responses noted upon subsequent exposure to sucralose. To further identify microbiome functions potentially linked to alterations in glucose tolerance in recipient mice of top and bottom sucralose-responder human microbiomes, we compared microbiome profiles of mouse recipients of top and bottom human sucralose responders collected on day 21 with those in mice receiving baseline pre-exposure samples from the same top and bottom sucralose-responsive humans. Indeed, mice receiving day 21 microbiome samples from top responders presented higher alpha diversity compared with those receiving baseline microbiomes from the same top responders or compared with mice receiving day 21 microbiomes from bottom responders (linear-mixed effects regression, p = 0.004; Figure 7H). A comparison between the baseline and day 21 mouse recipient microbiomes revealed 13 pathways significantly altered in the top responders' group and none in the bottom responder mice. All significantly altered pathways were less abundant on day 21 compared with baseline (Figure 7I; Table S7). Pathways related to the biosynthesis of fatty acids, already less abundant in mouse recipients of baseline top responder microbiomes compared with mouse recipients of baseline bottom responder microbiomes, became undetectable in mice transplanted with day 21 top responder microbiome (p = 0.012). The fold increase of two pathways (day 21/ baseline abundance) was significantly correlated with an increased glycemic response: sucrose degradation (Spearman r = -1, p = 0.0028, Figure 7J) and glycogen degradation (Spearman r = 1, p = 0.0028, Figure 7K). Taken together, these results suggest that the ability of the microbiome to respond to sucralose in altering host glucose tolerance may be mediated, at least in part, by the capacity of the bacteria to metabolize dietary and/or host-derived carbohydrates and utilize them for energy production. These results merit future causative validation in future studies. Discussion Our work provides evidence of human microbiome responsiveness to NNS and its ability to transmit, in specific configurations, downstream effects on the host glucose tolerance. As such, and in contrast to the common notion suggesting that NNS are metabolically inert, these data suggest that the human gut microbiome may constitute a "responsiveness hub" enabling, in some individuals, the transmission of NNS effects on human physiology. Similarly, other "modern" food additives such as dietary emulsifiers ( Chassaing et al., 2015 * Chassaing B. * Koren O. * Goodrich J.K. * Poole A.C. * Srinivasan S. * Ley R.E. * Gewirtz A.T. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature. 2015; 519: 92-96 * Crossref * PubMed * Scopus (998) * Google Scholar ; Tang et al., 2013 * Tang W.H.W. * Wang Z. * Levison B.S. * Koeth R.A. * Britt E.B. * Fu X. * Wu Y. * Hazen S.L. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 2013; 368: 1575-1584 * Crossref * PubMed * Scopus (1881) * Google Scholar ), food preservatives ( Tirosh et al., 2019 * Tirosh A. * Calay E.S. * Tuncman G. * Claiborn K.C. * Inouye K.E. * Eguchi K. * Alcala M. * Rathaus M. * Hollander K.S. * Ron I. * et al. The short-chain fatty acid propionate increases glucagon and FABP4 production, impairing insulin action in mice and humans. Sci. Transl. Med. 2019; 11https://doi.org/10.1126/ scitranslmed.aav0120 * Crossref * PubMed * Scopus (104) * Google Scholar ), and colorants ( He et al., 2021 * He Z. * Chen L. * Catalan-Dibene J. * Bongers G. * Faith J.J. * Suebsuwong C. * DeVita R.J. * Shen Z. * Fox J.G. * Lafaille J.J. * Lira S.A. Food colorants metabolized by commensal bacteria promote colitis in mice with dysregulated expression of interleukin-23. Cell Metab. 2021; 33: 1358-1371.e5 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (10) * Google Scholar ) have been suggested to impact the microbiome and, in some cases, mediate downstream host metabolic effects. Interestingly, although the small amount of vehicle glucose incorporated into the NNS sachets and consumed by all participants in the NNS groups was comparable to the amount of glucose consumed by participants in the vehicle group, plasma insulin levels rose during supplementation only in the stevia and glucose vehicle groups. These results suggest a possible blunting of glucose-stimulated insulin secretion, leading to elevated glycemia in participants consuming saccharin or sucralose (with glucose as a vehicle). Notably, coupling of NNS with a caloric sweetener was reported to result in a higher insulin response compared with NNS alone ( Dalenberg et al., 2020 * Dalenberg J.R. * Patel B.P. * Denis R. * Veldhuizen M.G. * Nakamura Y. * Vinke P.C. * Luquet S. * Small D.M. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metab. 2020; 31: 493-502.e7 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (50) * Google Scholar ). The putative impacts of NNS on insulin sensitivity with and without a carbohydrate moiety should be addressed in further studies under glucose challenge conditions. Notably, all four tested NNS (saccharin, sucralose, aspartame, and stevia) significantly and distinctly altered the human intestinal and oral microbiome, as would be expected for these chemically diverse compounds. Such an effect was not observed in the two control groups. Sucralose ( Olivier-Van Stichelen et al., 2019 * Olivier-Van Stichelen S. * Rother K.I. * Hanover J.A. Maternal exposure to non-nutritive sweeteners impacts progeny's metabolism and microbiome. Front. Microbiol. 2019; 10: 1360 * Crossref * PubMed * Scopus (39) * Google Scholar ; Uebanso et al., 2017 * Uebanso T. * Ohnishi A. * Kitayama R. * Yoshimoto A. * Nakahashi M. * Shimohata T. * Mawatari K. * Takahashi A. Effects of low-dose non-caloric sweetener consumption on gut microbiota in mice. Nutrients. 2017; 9: 560https://doi.org/10.3390/nu9060560 * Crossref * Scopus (0) * Google Scholar ), saccharin ( Serrano et al., 2021 * Serrano J. * Smith K.R. * Crouch A.L. * Sharma V. * Yi F. * Vargova V. * LaMoia T.E. * Dupont L.M. * Serna V. * Tang F. * et al. High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice. Microbiome. 2021; 9: 11 * Crossref * PubMed * Scopus (11) * Google Scholar ), and stevia metabolites ( Wheeler et al., 2008 * Wheeler A. * Boileau A.C. * Winkler P.C. * Compton J.C. * Prakash I. * Jiang X. * Mandarino D.A. Pharmacokinetics of rebaudioside A and stevioside after single oral doses in healthy men. Food Chem. Toxicol. 2008; 46: S54-S60 * Crossref * PubMed * Scopus (0) * Google Scholar ) are found in stool of NNS-supplemented animals and humans, suggesting that direct interaction of these NNS with the intestinal microbiome is fully plausible. Sucralose is poorly absorbed, and thus, the majority of orally supplemented sucralose reaches the colon, and subsequently, most, but not all, is excreted unchanged in feces ( John et al., 2000 * John B.A. * Wood S.G. * Hawkins D.R. The pharmacokinetics and metabolism of sucralose in the mouse. Food Chem. Toxicol. 2000; 38: S107-S110 * Crossref * PubMed * Google Scholar ; Roberts et al., 2000 * Roberts A. * Renwick A.G. * Sims J. * Snodin D.J. Sucralose metabolism and pharmacokinetics in man. Food Chem. Toxicol. 2000; 38: S31-S41 * Crossref * PubMed * Scopus (0) * Google Scholar ; Sims et al., 2000 * Sims J. * Roberts A. * Daniel J.W. * Renwick A.G. The metabolic fate of sucralose in rats. Food Chem. Toxicol. 2000; 38: S115-S121 * Crossref * PubMed * Scopus (0) * Google Scholar ; Wood et al., 2000 * Wood S.G. * John B.A. * Hawkins D.R. The pharmacokinetics and metabolism of sucralose in the dog. Food Chem. Toxicol. 2000; 38: S99-S106 * Crossref * PubMed * Google Scholar ). The metabolic fate of the remaining fraction is currently unknown, although sucralose-related metabolites of unknown function have been identified in feces and adipose tissue ( Bornemann et al., 2018 * Bornemann V. * Werness S.C. * Buslinger L. * Schiffman S.S. Intestinal metabolism and bioaccumulation of sucralose in adipose tissue in the rat. J. Toxicol. Environ. Health A. 2018; 81: 913-923 * Crossref * PubMed * Scopus (14) * Google Scholar ). Interestingly, inter-subject variability in fecal excretion was reported ( Roberts et al., 2000 * Roberts A. * Renwick A.G. * Sims J. * Snodin D.J. Sucralose metabolism and pharmacokinetics in man. Food Chem. Toxicol. 2000; 38: S31-S41 * Crossref * PubMed * Scopus (0) * Google Scholar ; Sims et al., 2000 * Sims J. * Roberts A. * Daniel J.W. * Renwick A.G. The metabolic fate of sucralose in rats. Food Chem. Toxicol. 2000; 38: S115-S121 * Crossref * PubMed * Scopus (0) * Google Scholar ; Sylvetsky et al., 2017c * Sylvetsky A.C. * Bauman V. * Blau J.E. * Garraffo H.M. * Walter P.J. * Rother K.I. Plasma concentrations of sucralose in children and adults. Toxicol. Environ. Chem. 2017; 99: 535-542 * Crossref * PubMed * Scopus (11) * Google Scholar ; Wood et al., 2000 * Wood S.G. * John B.A. * Hawkins D.R. The pharmacokinetics and metabolism of sucralose in the dog. Food Chem. Toxicol. 2000; 38: S99-S106 * Crossref * PubMed * Google Scholar ), potentially underlying heterogeneity in metabolic responses to sucralose. Saccharin is slowly absorbed from the gut to the bloodstream, and a minority of ingested saccharin (5%-15%) is excreted in feces, mostly unchanged ( Ball et al., 1974 * Ball L.M. * Renwick A.G. * Williams R.T. The fate of [14C]saccharin in rats chronically fed on saccharin. Biochem. Soc. Trans. 1974; 2: 1084-1086 * Crossref * Scopus (4) * Google Scholar ; Renwick, 1985 * Renwick A.G. The disposition of saccharin in animals and man--a review. Food Chem. Toxicol. 1985; 23: 429-435 * Crossref * PubMed * Scopus (67) * Google Scholar ; Sweatman et al., 1981 * Sweatman T.W. * Renwick A.G. * Burgess C.D. The pharmacokinetics of saccharin in man. Xenobiotica. 1981; 11: 531-540 * Crossref * PubMed * Google Scholar ). The long absorption time and poor bioavailability support possible interactions with the microbiome. Degradation of steviol glycosides by gut bacteria is an established component of their metabolism ( Magnuson et al., 2016 * Magnuson B.A. * Carakostas M.C. * Moore N.H. * Poulos S.P. * Renwick A.G. Biological fate of low-calorie sweeteners. Nutr. Rev. 2016; 74: 670-689 * Crossref * PubMed * Scopus (0) * Google Scholar ), although some species may be more proficient than others in performing this task ( Gardana et al., 2003 * Gardana C. * Simonetti P. * Canzi E. * Zanchi R. * Pietta P. Metabolism of stevioside and rebaudioside A from stevia rebaudiana extracts by human microflora. J. Agric. Food Chem. 2003; 51: 6618-6622 * Crossref * PubMed * Scopus (159) * Google Scholar ), and thus, pre-exposure microbiome heterogeneity may conduce to differential responses to stevia. In contrast to sucralose, saccharin, and stevia, aspartame is metabolized by host enzymes in the proximal regions of the gastrointestinal tract ( Magnuson et al., 2016 * Magnuson B.A. * Carakostas M.C. * Moore N.H. * Poulos S.P. * Renwick A.G. Biological fate of low-calorie sweeteners. Nutr. Rev. 2016; 74: 670-689 * Crossref * PubMed * Scopus (0) * Google Scholar ). Thus, the mechanisms through which aspartame modulated the fecal microbiome of human participants in this study and in previous reports in animal models ( Nettleton et al., 2020 * Nettleton J.E. * Cho N.A. * Klancic T. * Nicolucci A.C. * Shearer J. * Borgland S.L. * Johnston L.A. * Ramay H.R. * Noye Tuplin E. * Chleilat F. * et al. Maternal low-dose aspartame and stevia consumption with an obesogenic diet alters metabolism, gut microbiota and mesolimbic reward system in rat dams and their offspring. Gut. 2020; 69: 1807-1817 * Crossref * PubMed * Scopus (22) * Google Scholar ; Palmnas et al., 2014 * Palmnas M.S.A. * Cowan T.E. * Bomhof M.R. * Su J. * Reimer R.A. * Vogel H.J. * Hittel D.S. * Shearer J. Low-dose aspartame consumption differentially affects gut microbiota-host metabolic interactions in the diet-induced obese rat. PLoS One. 2014; 9: e109841 * Crossref * PubMed * Scopus (0) * Google Scholar ) merit further study. NNS may impact gut commensals through several direct and indirect mechanisms, some highlighted by our study's results. First, NNS may induce microbial growth inhibition, as was shown for cultured E. coli ( Harpaz et al., 2018 * Harpaz D. * Yeo L.P. * Cecchini F. * Koon T.H.P. * Kushmaro A. * Tok A.I.Y. * Marks R.S. * Eltzov E. Measuring artificial sweeteners toxicity using a bioluminescent bacterial panel. Molecules. 2018; 23: 2454 * Crossref * Scopus (32) * Google Scholar ; Wang et al., 2018 * Wang Q.P. * Browman D. * Herzog H. * Neely G.G. Non-nutritive sweeteners possess a bacteriostatic effect and alter gut microbiota in mice. PLoS One. 2018; 13: e0199080 * PubMed * Google Scholar ), pathogens ( Sunderhauf et al., 2020 * Sunderhauf A. * Pagel R. * Kunstner A. * Wagner A.E. * Rupp J. * Ibrahim S.M. * Derer S. * Sina C. Saccharin supplementation inhibits bacterial growth and reduces experimental colitis in mice. Nutrients. 2020; 12: 1122 * Crossref * Scopus (7) * Google Scholar ), oral ( Prashant e t al., 2012 * Prashant G.M. * Patil R.B. * Nagaraj T. * Patel V.B. The antimicrobial activity of the three commercially available intense sweeteners against common periodontal pathogens: an in vitro study. J. Contemp. Dent. Pract. 2012; 13: 749-752 * Crossref * PubMed * Scopus (24) * Google Scholar ), and environmental ( Omran et al., 2013 * Omran A. * Ahearn G. * Bowers D. * Swenson J. * Coughlin C. Metabolic effects of sucralose on environmental bacteria. J. Toxicol. 2013; 2013: 372986 * Crossref * PubMed * Scopus (23) * Google Scholar ) bacteria, or of commensals of the rat cecal content ( Naim et al., 1985 * Naim M. * Zechman J.M. * Brand J.G. * Kare M.R. * Sandovsky V. Effects of sodium saccharin on the activity of trypsin, chymotrypsin, and amylase and upon bacteria in small intestinal contents of rats. Proc. Soc. Exp. Biol. Med. 1985; 178: 392-401 * Crossref * PubMed * Google Scholar ) and human stool ( Vamanu et al., 2019 * Vamanu E. * Pelinescu D. * Gatea F. * Sarbu I. Altered in vitro metabolomic response of the human microbiota to sweeteners. Genes (Basel). 2019; 10: 535 * Crossref * Scopus (0) * Google Scholar ). In vivo studies similarly demonstrated a reduction in total fecal bacterial loads in animals treated with sucralose ( Abou-Donia et al., 2008 * 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 A. 2008; 71: 1415-1429 * Crossref * PubMed * Scopus (210) * Google Scholar ) or saccharin ( Sunderhauf et al., 2020 * Sunderhauf A. * Pagel R. * Kunstner A. * Wagner A.E. * Rupp J. * Ibrahim S.M. * Derer S. * Sina C. Saccharin supplementation inhibits bacterial growth and reduces experimental colitis in mice. Nutrients. 2020; 12: 1122 * Crossref * Scopus (7) * Google Scholar ). The bacterial targets affected by NNS are not fully identified and potentially include disruption of quorum sensing ( Bian et al., 2017c * Bian X. * Chi L. * Gao B. * Tu P. * Ru H. * Lu K. Gut microbiome response to sucralose and its potential role in inducing liver inflammation in mice. Front. Physiol. 2017; 8: 487https://doi.org/10.3389/fphys.2017.00487 * Crossref * PubMed * Scopus (114) * Google Scholar ; Markus et al., 2021 * Markus V. * Share O. * Shagan M. * Halpern B. * Bar T. * Kramarsky-Winter E. * Terali K. * Ozer N. * Marks R.S. * Kushmaro A. * Golberg K. Inhibitory effects of artificial sweeteners on bacterial quorum sensing. Int. J. Mol. Sci. 2021; 22: 9863 * Crossref * PubMed * Scopus (1) * Google Scholar ), triggering of SOS responses ( Yu et al., 2021 * Yu Z. * Wang Y. * Lu J. * Bond P.L. * Guo J. Nonnutritive sweeteners can promote the dissemination of antibiotic resistance through conjugative gene transfer. ISME J. 2021; 15: 2117-2130 * Crossref * PubMed * Scopus (41) * Google Scholar ), increased membrane permeability ( Yu et al., 2021 * Yu Z. * Wang Y. * Lu J. * Bond P.L. * Guo J. Nonnutritive sweeteners can promote the dissemination of antibiotic resistance through conjugative gene transfer. ISME J. 2021; 15: 2117-2130 * Crossref * PubMed * Scopus (41) * Google Scholar ), increased mutation frequency ( Qu et al., 2017 * Qu Y. * Li R. * Jiang M. * Wang X. Sucralose increases antimicrobial resistance and stimulates recovery of Escherichia coli mutants. Curr. Microbiol. 2017; 74: 885-888 * Crossref * PubMed * Scopus (0) * Google Scholar ), inhibition of glucose/sucrose transport to the bacterial cell ( Omran et al., 2013 * Omran A. * Ahearn G. * Bowers D. * Swenson J. * Coughlin C. Metabolic effects of sucralose on environmental bacteria. J. Toxicol. 2013; 2013: 372986 * Crossref * PubMed * Scopus (23) * Google Scholar ; Pfeffer et al., 1985 * Pfeffer M. * Ziesenitz S.C. * Siebert G. Acesulfame K, cyclamate and saccharin inhibit the anaerobic fermentation of glucose by intestinal bacteria. Z. Ernahrungswiss. 1985; 24: 231-235 * Crossref * PubMed * Scopus (27) * Google Scholar ), inhibition of sucrose enzymatic degradation or glucose fermentation ( Omran et al., 2013 * Omran A. * Ahearn G. * Bowers D. * Swenson J. * Coughlin C. Metabolic effects of sucralose on environmental bacteria. J. Toxicol. 2013; 2013: 372986 * Crossref * PubMed * Scopus (23) * Google Scholar ; Pfeffer et al., 1985 * Pfeffer M. * Ziesenitz S.C. * Siebert G. Acesulfame K, cyclamate and saccharin inhibit the anaerobic fermentation of glucose by intestinal bacteria. Z. Ernahrungswiss. 1985; 24: 231-235 * Crossref * PubMed * Scopus (27) * Google Scholar ), and a reduction in the abundance of phosphotransferase system (PTS) genes involved in the transport of sugars to the bacterial cell, in microbiome cultures and in mice exposed to saccharin ( Suez et al., 2014 * Suez J. * Korem T. * Zeevi D. * Zilberman-Schapira G. * Thaiss C.A. * Maza O. * Israeli D. * Zmora N. * Gilad S. * Weinberger A. * et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 * Crossref * PubMed * Scopus (1108) * Google Scholar ). In the current study, sucralose exposure resulted in reduced abundance of nucleotide biosynthesis genes, which might be linked to inhibited bacterial replication. In contrast, the abundance of genes related to mixed-acid fermentation and TCA cycle increased during sucralose supplementation. TCA metabolites were also elevated in plasma during sucralose supplementation, suggestive of possible microbiome contributions. Elevated plasma levels of TCA metabolites has been associated with impaired glycemic control ( Fiehn et al., 2010 * Fiehn O. * Garvey W.T. * Newman J.W. * Lok K.H. * Hoppel C.L. * Adams S.H. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010; 5: e15234 * Crossref * PubMed * Scopus (301) * Google Scholar ; Guasch-Ferre et al., 2020 * Guasch-Ferre M. * Santos J.L. * Martinez-Gonzalez M.A. * Clish C.B. * Razquin C. * Wang D. * Liang L. * Li J. * Dennis C. * Corella D. * et al. Glycolysis/gluconeogenesis- and tricarboxylic acid cycle-related metabolites, Mediterranean diet, and type 2 diabetes. Am. J. Clin. Nutr. 2020; 111: 835-844 * Crossref * PubMed * Scopus (0) * Google Scholar ). In addition, some bacterial species may bloom in the presence of NNS ( Palmnas et al., 2014 * Palmnas M.S.A. * Cowan T.E. * Bomhof M.R. * Su J. * Reimer R.A. * Vogel H.J. * Hittel D.S. * Shearer J. Low-dose aspartame consumption differentially affects gut microbiota-host metabolic interactions in the diet-induced obese rat. PLoS One. 2014; 9: e109841 * Crossref * PubMed * Scopus (0) * Google Scholar ; Rodriguez-Palacios et al., 2018 * Rodriguez-Palacios A. * Harding A. * Menghini P. * Himmelman C. * Retuerto M. * Nickerson K.P. * Lam M. * Croniger C.M. * McLean M.H. * Durum S.K. * et al. The artificial sweetener Splenda promotes gut Proteobacteria, dysbiosis, and myeloperoxidase reactivity in Crohn's disease-like ileitis. Inflamm. Bowel Dis. 2018; 24: 1005-1020 * Crossref * PubMed * Scopus (91) * Google Scholar ). Blooming may be mediated by an unoccupied niche, depleted of species inhibited by NNS. Alternatively, some gut bacteria can utilize NNS that reach the lower gut (e.g., saccharin and sucralose) as an energy or carbon source, thus gaining a growth advantage in their presence ( Schiffman, and Rother, 2013 * Schiffman S.S. * Rother K.I. Sucralose, a synthetic organochlorine sweetener: overview of biological issues. J. Toxicol. Environ. Health B Crit. Rev. 2013; 16: 399-451 * Crossref * PubMed * Scopus (0) * Google Scholar ). Such mechanism was mainly studied to date in environmental bacteria ( Labare, and Alexander, 1995 * Labare M.P. * Alexander M. Microbial cometabolism of sucralose, a chlorinated disaccharide, in environmental samples. Appl. Microbiol. Biotechnol. 1994; 42: 173-178 * Crossref * PubMed * Scopus (38) * Google Scholar ) or shown to exist in a single species in the presence of oxygen ( Schleheck, and Cook, 2003 * Schleheck D. * Cook A.M. Saccharin as a sole source of carbon and energy for Sphingomonas xenophaga SKN. Arch. Microbiol. 2003; 179: 191-196 * Crossref * PubMed * Scopus (15) * Google Scholar ). One hint for such a mechanism in our study relates to the detection of several metabolites in the plasma of saccharin-supplemented individuals that may stem from saccharin degradation. Exploring such degradation capacities merits future studies in gut-residing human commensals. Notably, NNS could potentially alter the microbiome through indirect, host-mediated effects. These include interaction between NNS and sweet and/or bitter taste receptors in the gut and downstream effects on the microbiome ( Turner et al., 2020 * Turner A. * Veysey M. * Keely S. * Scarlett C.J. * Lucock M. * Beckett E.L. Intense sweeteners, taste receptors and the gut microbiome: A metabolic health perspective. Int. J. Environ. Res. Public Health. 2020; 17: 4094 * Crossref * Scopus (7) * Google Scholar ) and possible effects of NNS on the immune system ( Bian et al., 2017a * Bian X. * Tu P. * Chi L. * Gao B. * Ru H. * Lu K. Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions. Food Chem. Toxicol. 2017; 107: 530-539 * Crossref * PubMed * Scopus (81) * Google Scholar , Bian et al., 2017c * Bian X. * Chi L. * Gao B. * Tu P. * Ru H. * Lu K. Gut microbiome response to sucralose and its potential role in inducing liver inflammation in mice. Front. Physiol. 2017; 8: 487https://doi.org/10.3389/fphys.2017.00487 * Crossref * PubMed * Scopus (114) * Google Scholar ; Cheng et al., 2021 * Cheng X. * Guo X. * Huang F. * Lei H. * Zhou Q. * Song C. Effect of different sweeteners on the oral microbiota and immune system of Sprague Dawley rats. AMB Express. 2021; 11: 8 * Crossref * PubMed * Scopus (1) * Google Scholar ; Martinez-Carrillo et al., 2019 * Martinez-Carrillo B.E. * Rosales-Gomez C.A. * Ramirez-Duran N. * Resendiz-Albor A.A. * Escoto-Herrera J.A. * Mondragon-Velasquez T. * Valdes-Ramos R. * Castillo-Cardiel A. Effect of chronic consumption of sweeteners on microbiota and immunity in the small intestine of young mice. Int. J. Food Sci. 2019; 2019: 9619020 * Crossref * PubMed * Scopus (9) * Google Scholar ). These merit further study. Collectively, our study suggests that commonly consumed NNS may not be physiologically inert in humans as previously contemplated, with some of their effects mediated indirectly through impacts exerted on distinct configurations of the human microbiome. We stress that these results should not be interpreted as calling for consumption of sugar, which is strongly linked to cardiometabolic diseases and other adverse health effects ( Malik and Hu, 2022 * Malik V.S. * Hu F.B. The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nature Reviews Endocrinology. 2022; 18: 205-218 * Crossref * PubMed * Scopus (14) * Google Scholar , Vos et al., 2017 * Vos M.B. * Kaar J.L. * Welsh J.A. * Van Horn L.V. * Feig D.I. * Anderson C.A. * Patel M.J. * Cruz Munos J. * Krebs N.F. * Xanthakos S.A. * Johnson R.K. Added sugars and cardiovascular disease risk in children: a scientific statement from the American Heart Association. Circulation. 2017; 135: e1017-e1034 * Crossref * PubMed * Scopus (270) * Google Scholar ). Unraveling molecular mechanisms and clinical consequences of NNS consumption on the human host and microbiome may enable to optimize dietary recommendations in preventing and treating hyperglycemia and its metabolic ramifications. Limitations of the study Limitations of our study include the inclusion of healthy, non-overweight, normoglycemic individuals, as NNS effects may differ between healthy and individuals with cardiometabolic diseases ( Nichol et al., 2019 * Nichol A.D. * Salame C. * Rother K.I. * Pepino M.Y. Effects of sucralose ingestion versus sucralose taste on metabolic responses to an oral glucose tolerance test in participants with normal weight and obesity: A randomized crossover trial. Nutrients. 2019; 12: 29 * Crossref * Scopus (8) * Google Scholar ), calling for further studies in these populations. In addition, the NNS in our study were administered as commercially available sachets containing a mixture of glucose and a given NNS. Microbiome and glycemic responses may differ when administered as commercial NNS sachets (containing carbohydrates as fillers) ( Romo-Romo et al., 2018 * Romo-Romo A. * Aguilar-Salinas C.A. * Brito-Cordova G.X. * Gomez-Diaz R.A. * Almeda-Valdes P. Sucralose decreases insulin sensitivity in healthy subjects: a randomized controlled trial. Am. J. Clin. Nutr. 2018; 108: 485-491 * Crossref * PubMed * Scopus (38) * Google Scholar ; Suez et al., 2014 * Suez J. * Korem T. * Zeevi D. * Zilberman-Schapira G. * Thaiss C.A. * Maza O. * Israeli D. * Zmora N. * Gilad S. * Weinberger A. * et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 * Crossref * PubMed * Scopus (1108) * Google Scholar ) in comparison with their purified forms ( Ahmad et al., 2020a * Ahmad S.Y. * Friel J.K. * MacKay D.S. The effect of the artificial sweeteners on glucose metabolism in healthy adults: a randomized, double-blinded, crossover clinical trial. Appl. Physiol. Nutr. Metab. 2020; 45: 606-612 * Crossref * PubMed * Scopus (13) * Google Scholar , Ahmad et al., 2020b * Ahmad S.Y. * Friel J. * Mackay D. The effects of non-nutritive artificial sweeteners, aspartame and sucralose, on the gut microbiome in healthy adults: secondary outcomes of a randomized double-blinded crossover clinical trial. Nutrients. 2020; 12: 3408 * Crossref * Scopus (12) * Google Scholar ; Kim et al., 20 20 * Kim Y. * Keogh J.B. * Clifton P.M. Consumption of a beverage containing aspartame and acesulfame K for two weeks does not adversely influence glucose metabolism in adult males and females: A randomized crossover study. Int. J. Environ. Res. Public Health. 2020; 17: 9049 * Crossref * Scopus (0) * Google Scholar ; Serrano et al., 2021 * Serrano J. * Smith K.R. * Crouch A.L. * Sharma V. * Yi F. * Vargova V. * LaMoia T.E. * Dupont L.M. * Serna V. * Tang F. * et al. High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice. Microbiome. 2021; 9: 11 * Crossref * PubMed * Scopus (11) * Google Scholar ; Thomson et al., 2019 * Thomson P. * Santibanez R. * Aguirre C. * Galgani J.E. * Garrido D. Short-term impact of sucralose consumption on the metabolic response and gut microbiome of healthy adults. Br. J. Nutr. 2019; 122: 856-862 * Crossref * PubMed * Scopus (0) * Google Scholar ). Indeed, Dalenberg et al. elegantly reported an adverse effect of short-term supplementation with sucralose on glycemic control, only when the NNS was coupled with a carbohydrate ( Dalenberg et al., 2020 * Dalenberg J.R. * Patel B.P. * Denis R. * Veldhuizen M.G. * Nakamura Y. * Vinke P.C. * Luquet S. * Small D.M. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metab. 2020; 31: 493-502.e7 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (50) * Google Scholar ). In line with this observation, we found that a combination of bulking glucose with saccharin or sucralose, but not glucose alone, resulted in an impaired glycemic response, whereas elevated plasma insulin was only observed in individuals supplemented with glucose alone or stevia. Notably, plasma insulin was not measured under fasting conditions, limiting the interpretation of these results. However, if such formulation distinction is true, NNS impacts should be compared in future controlled trials between consumers of carbohydrate-rich and carbohydrate-restrictive diets for their potential differential effects on human metabolic physiology. Of note, longer exposure periods (4-10 weeks) to pure NNS were suggested by some studies to negatively impact metabolic health even in the absence of a carbohydrate additive ( Bueno-Hernandez et al., 2020 * Bueno-Hernandez N. * Esquivel-Velazquez M. * Alcantara-Suarez R. * Gomez-Arauz A.Y. * Espinosa-Flores A.J. * de Leon-Barrera K.L. * Mendoza-Martinez V.M. * Sanchez Medina G.A. * Leon-Hernandez M. * Ruiz-Barranco A. * et al. Chronic sucralose consumption induces elevation of serum insulin in young healthy adults: a randomized, double blind, controlled trial. Nutr. J. 2020; 19: 32 * Crossref * PubMed * Scopus (8) * Google Scholar ; Higgins, and Mattes, 2019 * Higgins K.A. * Mattes R.D. A randomized controlled trial contrasting the effects of 4 low-calorie sweeteners and sucrose on body weight in adults with overweight or obesity. Am. J. Clin. Nutr. 2019; 109: 1288-1301 * Crossref * PubMed * Scopus (0) * Google Scholar ; Lertrit et al., 2018 * Lertrit A. * Srimachai S. * Saetung S. * Chanprasertyothin S. * Chailurkit L.O. * Areevut C. * Katekao P. * Ongphiphadhanakul B. * Sriphrapradang C. Effects of sucralose on insulin and glucagon-like peptide-1 secretion in healthy subjects: a randomized, double-blind, placebo-controlled trial. Nutrition. 2018; 55-56: 125-130 * Crossref * PubMed * Scopus (42) * Google Scholar ; Mendez-Garcia et al., 2022 * Mendez-Garcia L.A. * Bueno-Hernandez N. * Cid-Soto M.A. * De Leon K.L. * Mendoza-Martinez V.M. * Espinosa-Flores A.J. * Carrero-Aguirre M. * Esquivel-Velazquez M. * Leon-Hernandez M. * Viurcos-Sanabria R. * et al. Ten-week sucralose consumption induces gut dysbiosis and altered glucose and insulin levels in healthy young adults. Microorganisms. 2022; 10: 434 * Crossref * PubMed * Scopus (1) * Google Scholar ). As such, a longer exposure period than the one utilized in our study may be required to fully assess the potential health ramifications mediated by the altered microbiome upon consumption of different NNS. Likewise, the NNS doses tested in our study were 240 mg (aspartame, ~8% ADI), 180 mg (saccharin, ~20% ADI), 102 mg (sucralose, 34% ADI), and 180 mg (stevia, ~75% ADI). Future studies may determine whether even lower doses may differentially impact the microbiome and host glycemic responses. STARMethods Key resources table Tabled 1 REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Insulin ELISA Crystal Chem Cat #90095 GLP-1 ELISA Crystal Chem Cat #81506 Chemicals, peptides, and recombinant proteins D(+)-Glucose monohydrate (for GTT in J.T. Baker 113 mice) Commercial NNS: saccharin N/A Glucose for human trial Floris N/A Commercial NNS: sucralose N/A Commercial NNS: aspartame N/A Commercial NNS: stevia N/A Critical commercial assays NextSeq 500/550 High Output v2 kit (75 cycles), for Metagenome shotgun Illumina Cat# 20024906 sequencing DNeasy PowerLyzer PowerSoil Kit QIAGEN Cat# 20-27100-12-EP Experimental models: Organisms / strains Germ-free Swiss-Webster males 7-9 weeks Weizmann of age Institute of N/A Science Deposited data Stool and oral metagenomics sequencing PRJEB47383 N/A Software and algorithms Code package for TCAM analysis https://github.com/UriaMorP/ mprod_package * Open table in a new tab Resource availability Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Eran Elinav ( [email protected] ). Materials availability This study did not generate new unique reagents. Data and code availability Metagenomics sequencing data have been deposited at the European Nucleotide Archive (ENA) and are publicly available as of the date of publication (accession number PRJEB47383). Accession number is also listed in the key resources table. The code package for the TCAM analysis can be found in the Github repository https://github.com/ UriaMorP/mprod_package. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Experimental model and subject details Human subjects Human study design This was an open-label, multi-arm RCT that assessed the effects of short-term supplementation of non-caloric sweeteners on the microbiome, glucose tolerance, and additional health parameters, in healthy adults. The primary outcome was blood glucose levels, measured during standardized glucose tolerance tests (GTT) using a continuous glucose monitor (CGM). Secondary outcomes included microbiome readouts in stool and oral samples, and additional anthropometrics. The study protocol and associated procedures were approved by Weizmann Institute of Science Bioethics and Embryonic Stem Cell Research oversight committee (IRB approval number 170-2), and reported to http://clinicaltrials.gov/, Registration Number NCT03708939. Written informed consent was obtained from all subjects. Inclusion and exclusion criteria Eligible participants were males and females aged 18-70 able to provide informed consent and operate a glucometer and a smartphone. We excluded participants who consumed any quantity of NNS-containing foods or beverages in the six months prior to the trial initiation using an online custom-made food frequency questionnaire, further validated over the phone by a Clinical Research Associate (CRA). Participants were required to provide their consumption frequency (never; once a month; more than once a month; once a week; more than once a week; once a day; more than once a day) for each of the following NNS-containing products on the Israeli market: carbonated diet drinks; sugar-free energy drinks; non-carbonated diet drinks (diet iced tea, diet fruit juices, diet syrups); diet yogurt; sugar-free chewing gum; protein powders/gainers; sugar-free ice-cream; diet/low/sugar-free cookies, cakes, pastry; low/sugar-free jam, marmalade; sachets/tablets/drops of non-nutritive sweeteners; low/sugar-free ketchup or other sauces; sugar-free halva; diet bread / pita bread; diet syrups (chocolate, maple...); sugar-free chocolate; low/sugar-free cereals; any product labeled as "diet / low / sugar-free". Additional exclusion criteria included: (i) pregnancy or fertility treatments; (ii) breastfeeding (including baby to breast and bottle feeding mother's expressed breast milk); (iii) usage of antibiotics or antifungals within three months prior to participation; (iv) BMI < 18 or > 28; (v) pre-diagnosed type 1 or type 2 diabetes mellitus or treatment with anti-diabetic medication; (vi) chronically active inflammatory or neoplastic disease in the three years prior to enrollment; (vii) chronic gastrointestinal disorder, including inflammatory bowel disease and celiac disease; (viii) active neuropsychiatric disorder; (ix) myocardial infarction or cerebrovascular accident in the six months prior to participation; (x) coagulation disorders; (xi) chronic immunosuppressive medication usage; (xii) alcohol or substance abuse; (xiii) bariatric surgery; (xiv) phenylketonuria excluded randomization to the aspartame group. Screening for individuals meeting the aforementioned criteria was achieved using an online questionnaire, validated over the phone by a CRA or a medical doctor. The most frequent exclusion criterion was NNS consumption (Figure 1B). Participants Between 2018-2020, a total of 131 eligible participants were invited in groups of 4-12 to an initiation meeting at the Weizmann Institute of Science, during which they received full details regarding the study aims, protocol, and risks. After the meeting, all 131 individuals consented to participate, filled an informed consent form, and were randomized to six intervention arms: aspartame, sucralose, saccharin, stevia, glucose vehicle, or no supplement control (see the Consolidated Standards of Reporting Trials (CONSORT) flow diagram in Figure 1B). Seven participants decided to prematurely terminate their participation for the following reasons (Figure 1B): positive result in a pregnancy test conducted during the baseline week (N=1, saccharin); dislike for the taste of the NNS (N=1, sucralose); nausea after performing GTTs (N=1, NSC); pain and minor bleeding during sensor insertion (N=1, stevia); difficulty with adherence to the study protocol (N=3, two in the glucose vehicle group and one in the NSC group). All participants withdrew prior to day 14, thus an intention-to-treat analysis was not feasible. Four additional participants were excluded from the analysis after the trial was completed due to insufficient data for the primary outcome, as follows: mishandling of the CGM resulting in no recorded glucose events (N=1, stevia); less than four valid recorded GTTs (out of nine, see GTT exclusion criteria below, N=3, one each in the stevia, glucose, and NSC groups). Cohort details A total of 120 participants were included in the analysis, of which 65% were female, median age 29.95 (IQR 26.93-35.23). No baseline differences were found between the groups in the following parameters (Table S1): Weight, BMI, waist-hip ratio, %HbA1c, CRP, total cholesterol, HDL cholesterol, systolic blood pressure, diastolic blood pressure, heart rate, ALT, AST (Kruskal-Wallis); dietary habits, smoking (Chi-square). Animals Germ-free mice were used to causally link between microbiome of NNS-consuming humans and glucose tolerance. All mice were Swiss-Webster WT adult (seven- to nine-week-old) males and served as recipients for fecal microbiome transplants from human donors. All animal studies were approved by the Weizmann Institute of Science Institutional Animal Care and Usage Committee (IACUC), application number 13250419-3. Method details Human trial experimental procedures During an introductory meeting at the Weizmann Institute, consenting individuals were connected to a continuous glucose monitor (CGM; FreeStyle Libre, Abbott) and anthropometric, blood pressure and heart-rate measurements were taken by a Clinical Research Associate (CRA) or a certified nurse, as well as a non-fasting blood test used for the blood works detailed in Table S1, as well as for insulin and GLP-1 using commercial ELISA kits (Crystal Chem). This was considered as day 0 (Figure 1). Participants arrived for two additional identical sessions on days 14 and 28, which was the last day of the trial. The CGM was replaced on day 14 according to the manufacturer's instructions. Participants were provided with a kit to sample their microbiome and perform glucose tolerance tests at home on pre-determined days. On days 8-21, participants in all groups except NSC consumed six commercially available sachets of NNS (with glucose as a vehicle bulking agent) or the equivalent amount of glucose daily, dissolved in water. NNS and glucose were provided by the researchers during the introductory meeting. Throughout the 28 days of the trial, participants were instructed to record all daily activities, including standardized and real-life meals, in real-time using their smartphones; meals were recorded with exact components and weights. For oral microbiome profiling, participants serially swabbed their buccal cavity in the morning on pre-determined days during the trial (eight samples per participant), following tooth brushing, but before consumption of food or usage of mouthwash. Non-nutritive sweeteners supplementation in humans Participants were supplied with sachets of commercial formulations of NNS used for sweetening hot beverages, all containing glucose as a bulking agent. Common, commercially available sachets were chosen in order to resemble real-life intake and maximize adherence to the protocol and acceptability of the supplement. The distinct flavor of NNS and glucose rendered blinding unfeasible. Participants consumed six sachets a day during the exposure period, resulting in the following daily doses: aspartame 0.24g & 5.76g glucose, saccharin 0.18g & 5.82g glucose, sucralose 0.102g & 5.898g glucose, stevia (steviol glycosides) 0.18g & 5.82g glucose. These doses are below the acceptable daily intakes, correspondingly: 50mg/kg, 15mg/kg, 5mg/kg, 4mg/kg. The glucose vehicle group was supplemented with 5g of glucose daily. All groups received the same number of sachets per day to allow comparison to the glucose vehicle group. Participants were instructed to consume two sachets of NNS dissolved in water three times during the day: morning, afternoon, and evening. Drinking of NNS was allowed either with or without meals. Participants recorded intake in real-time using a dedicated smartphone app. A CRA reviewed the logs daily to guarantee adherence to the protocol. Glucose tolerance tests in humans Participants were provided with 50 g of glucose (Floris) to perform GTT at home, with the following instructions: (A) at least seven and no more than fourteen hours without any food, supplements, physical activity or any drink other than water prior to GTT initiation; (B) the entire amount of glucose should be dissolved in a cup of water without any supplements and consumed in less than two minutes; (C) no physical activity, food or drinks other than water allowed during two hours after initiation; (D) GTT initiation should be recorded on the smartphone app, and data from the glucose sensor should be downloaded frequently to the reader. Participants received text message reminders on the days of the GTT. Out of 1080 expected GTTs, 5.5% (n= 60) were not performed (n=23) or performed but data were not recorded (n=37). Sensor malfunctions resulted in the loss of 1% of GTTs (n= 11). In addition, 2.6% of GTTs (n=28) were excluded from the analysis for not meeting the aforementioned requirements, as follows: food consumed during GTT (n=14), physical activity performed during GTT (n =5), fasting exceeds fifteen hours (n=4), consumption of glucose took >10 minutes (n=3), participant took a glucose-lowering medication (n= 2). There were no significant differences (Chi-square) in the number of missing/excluded GTTs between phases (P=0.86) or treatment groups (P=0.61). CGM Coefficient of variance (CoV) analysis Daily CoV was evaluated for each participant by dividing the daily standard deviation, considering all glucose measurements of the day, by the daily average. The significance of the trajectory changes in CoV was tested using LMER with participants as random effect and testing for the interaction between time and groups. Adverse events Few minor adverse events were reported (n=5), all during the baseline week prior to supplementation with NNS: diarrhea after first GTT (n= 2, one in the aspartame group and the other in the NSC group); common cold-like symptoms for several days without need for medical care (n= 2, sucralose and saccharin); mild transient pain in the CGM insertion site (n=1, aspartame). Diet and activity logs analysis Throughout the trial period, participants logged the following activities on an in-house developed smartphone application ( Zeevi et al., 2015 * Zeevi D. * Korem T. * Zmora N. * Israeli D. * Rothschild D. * Weinberger A. * Ben-Yacov O. * Lador D. * Avnit-Sagi T. * Lotan-Pompan M. * et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015; 163: 1079-1094 * Abstract * Full Text * Full Text PDF * PubMed * Scopus (1202) * Google Scholar ): sleep and wakeup time, physical activity (type, duration and intensity), meals, snacks, and drinks (ingredients and quantities), and medications. Participants were monitored at near-real-time for compliance in recording food and activity and contacted by phone as needed. To limit confounding of the dietary analysis by poor or insufficient logging of meals, we included in the final analysis only participants that had at least 20 days with at least 1,000 kcal logged per day; the number of analyzed individuals was not significantly different between the groups. Glucose tolerance tests in conventionalized germ-free mice Germ-free Swiss-Webster WT adult (seven to nine-week-old) male mice served as recipients for fecal microbiome transplants and were housed in sterile isolators (Park Bioservices). Fecal samples from human donors were frozen immediately after collection and were stored in a -80C freezer prior to processing. Two hundred mg of frozen stool was resuspended in 5 ml of sterile PBS under anaerobic conditions (Coy Laboratory Products, 75% N[2], 20% CO[2], 5% H[2]), vortexed for 3 min and allowed to settle by gravity for 2 min. The anaerobic homogenate was transferred to the animal facility in airtight Hungate tubes placed in anaerobic pouches (GasPak(tm) EZ Anaerobe Pouch System). Transplant into recipient mice was achieved by gavage with 200 ml of the supernatant. Mice were maintained on normal chow diet and water throughout the experiment. Mice were kept in iso-cages with sufficient food and water and were not handled until a glucose tolerance test was performed seven days post-conventionalization. Transplantation efficacy was determined by Bray-Curtis dissimilarity of each recipient mouse to its corresponding human donor, and was comparable between mouse recipients of top and bottom sucralose responder microbiomes and between mouse recipients of baseline and day 21 microbiomes. For glucose tolerance tests, mice were fasted for 6 h during the light phase, with free access to water. Blood from the tail vein was used to measure glucose levels using a glucometer (Bayer) immediately before and 15, 30, 60, 90 and 120 min after oral gavage feeding with 40 mg glucose (J. T. Baker). In all experiments, each experimental group consisted of at least two cages to minimize cage-effects. To ensure sterility of the mice for this primary readout, additional metabolic outputs that require baseline measurements (e.g., weight) were not recorded. Shotgun metagenomic sequencing Illumina libraries were prepared using a Nextera DNA Library Prep kit (Illumina, 20034198) according to the manufacturer's protocol and sequenced on an Illumina NextSeq platform with a read length of 75 bp (single-end) for all samples. Metagenomic analysis of stool and oral microbiome samples For human microbiome samples, data from the Illumina NextSeq sequencer were converted to fastq files with bcl2fastq, resulting in 1222 stool samples and 735 oral samples (9948552 +- 3440344 average and standard deviation of reads per sample). Sequences were then QC trimmed using Trimmomatic ( Bolger et al., 2014 * Bolger A.M. * Lohse M. * Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30: 2114-2120 * Crossref * PubMed * Scopus (24587) * Google Scholar ) with parameters PE -threads 10 -phred33 ILLUMINACLIP: NexteraPE-PE.fa:2:30:10 SLIDINGWINDOW:4:20 MINLEN:50 and host sequences were removed using KneadData with default parameters using the hg19 reference. We then subsampled all the samples to 1M sequences for stool samples and 250,000 for oral samples, removing all samples below this threshold and retaining 1182 stool and 713 oral samples. We removed 40 additional oral samples due to contamination (defined as abundance of >1% for six or more of the top ten abundant species in stool). Kraken2 ( Wood et al., 2019 * Wood D.E. * Lu J. * Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019; 20: 257 * Crossref * PubMed * Scopus (881) * Google Scholar ) was used for taxonomic analysis with a pre-built index database ( Meric et al., 2019 * Meric G. * Wick R.R. * Watts S.C. * Holt K.E. * Inouye M. Correcting index databases improves metagenomic studies. bioRxiv. 2019; https://doi.org/10.1101/712166 * Crossref * Scopus (0) * Google Scholar ) and Bracken ( Lu et al., 2017 * Lu J. * Breitwieser F.P. * Thielen P. * Salzberg S.L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 2017; 3: e104 * Crossref * Scopus (314) * Google Scholar ) was applied to estimate genus and species abundances. For functional annotations, we used both HUMAnN2 ( Franzosa et al., 2018 * Franzosa E.A. * McIver L.J. * Rahnavard G. * Thompson L.R. * Schirmer M. * Weingart G. * Lipson K.S. * Knight R. * Caporaso J.G. * Segata N. * Huttenhower C. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods. 2018; 15: 962-968 * Crossref * PubMed * Scopus (580) * Google Scholar ) as well as an in-house analytic pipeline. HUMAnN2 was used with the uniref90 ( Suzek et al., 2007 * Suzek B.E. * Huang H. * McGarvey P. * Mazumder R. * Wu C.H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics. 2007; 23: 1282-1288 * Crossref * PubMed * Scopus (730) * Google Scholar ) as the protein database and Chocophlan ( Franzosa et al., 2018 * Franzosa E.A. * McIver L.J. * Rahnavard G. * Thompson L.R. * Schirmer M. * Weingart G. * Lipson K.S. * Knight R. * Caporaso J.G. * Segata N. * Huttenhower C. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods. 2018; 15: 962-968 * Crossref * PubMed * Scopus (580) * Google Scholar ) as the nucleotide database; the path abundance unstratified output with MetaCyc ( Caspi et al., 2018 * Caspi R. * Billington R. * Fulcher C.A. * Keseler I.M. * Kothari A. * Krummenacker M. * Latendresse M. * Midford P.E. * Ong Q. * Ong W.K. * et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018; 46: D633-D639 * Crossref * PubMed * Scopus (378) * Google Scholar ) annotations was taken. The in-house pipeline consists of the following steps: first, we use diamond ( Buchfink et al., 2021 * Buchfink B. * Reuter K. * Drost H.G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods. 2021; 18: 366-368 * Crossref * PubMed * Scopus (122) * Google Scholar ) against the human gut IGC ( Li et al., 2014 * Li J. * Jia H. * Cai X. * Zhong H. * Feng Q. * Sunagawa S. * Arumugam M. * Kultima J.R. * Prifti E. * Nielsen T. * et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 2014; 32: 834-841 * Crossref * PubMed * Scopus (1036) * Google Scholar ) reference which was filtered to contain only KEGG ( Kanehisa, and Goto, 2000 * Kanehisa M. * Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28: 27-30 * Crossref * PubMed * Google Scholar ) annotated entries with parameters --max-hsps 1 -k 1 -e 0.0001. We then sum all the hits for each gene divided by its length and grouped to KEGG Orthologs and relative abundance is calculated. Pathways and Modules were computed using EMPANADA ( Manor, and Borenstein, 2017 * Manor O. * Borenstein E. Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome. Microbiome. 2017; 5: 19https://doi.org/10.1186/s40168-017-0231-4 * Crossref * PubMed * Scopus (14) * Google Scholar ). For dimensionality reduction of longitudinal microbiome data, participant microbiome trajectories were computed as the fold change from the baseline measurements averages, followed by application of M-product based Tensor Component Analysis (TCAM). Mouse samples were processed in a similar manner, with a total of 91 stool samples, and using the mm10 mouse genome reference. Mice samples were subsampled to 2.5M reads. Untargeted metabolomics Metabolite extraction Extraction and analysis of lipids and polar/semipolar metabolites was performed as previously described ( Malitsky et al., 2016 * Malitsky S. * Ziv C. * Rosenwasser S. * Zheng S. * Schatz D. * Porat Z. * Ben-Dor S. * Aharoni A. * Vardi A. Viral infection of the marine alga Emiliania huxleyi triggers lipidome remodeling and induces the production of highly saturated triacylglycerol. New Phytol. 2016; 210: 88-96 * Crossref * PubMed * Google Scholar ; Zheng et al., 20 15 * Zheng L. * Cardaci S. * Jerby L. * MacKenzie E.D. * Sciacovelli M. * Johnson T.I. * Gaude E. * King A. * Leach J.D.G. * Edrada-Ebel R. * et al. Fumarate induces redox-dependent senescence by modifying glutathione metabolism. Nat. Commun. 2015; 6: 6001 * Crossref * PubMed * Google Scholar ) with some modifications: 90ml of serum were extracted with 1 mL of a pre-cooled (-20@C) homogenous methanol:methyl-tert-butyl-ether (MTBE) 1:3 (v/v) mixture, containing following internal standards: 0.1 mg^*mL^-1 of Phosphatidylcholine (17:0/17:0) (Avanti), 0.4 mg^*mL ^-1 of Phosphatidylethanolamine (17:0/17:0, 0.15 nmol^*mL^-1 of Ceramide/Sphingoid Internal Standard Mixture I (Avanti, LM6002), 0.0267 mg/mL d5-TG Internal Standard Mixture I (Avanti, LM6000) and 0.1 mg^*mL^-1 Palmitic acid-13C (Sigma, 605573). The tubes were vortexed and then sonicated for 30 min in ice-cold sonication bath (taken for a brief vortex every 10 min). Then, UPLC-grade water: methanol (3:1, v/v) solution (0.5 mL) containing internal following standards: C13 and N15 labeled amino acids standard mix (Sigma) was added to the tubes followed by centrifugation. The upper, organic phase was transferred into 2 mL Eppendorf tube. The polar phase was re-extracted as described above, with 0.5 mL of MTBE. Both organic phases were combined and dried in speedvac and then stored at -80@C until analysis. For analysis, the lower, polar phase was used for polar and semipolar metabolite analysis was lyophilized and resuspended in 200 mL Methanol:DDW (50:50). LC-MS for semipolar metabolites processing Metabolic profiling of semipolar phase was performed using Waters ACQUITY UPLC system coupled to a Vion IMS QTof mass spectrometer (Waters Corp., MA, USA). The LC separation was as previously described ( Itkin et al., 2011 * Itkin M. * Rogachev I. * Alkan N. * Rosenberg T. * Malitsky S. * Masini L. * Meir S. * Iijima Y. * Aoki K. * de Vos R. * et al. Glycoalkaloid METABOLISM1 is required for steroidal alkaloid glycosylation and prevention of phytotoxicity in tomato. Plant Cell. 2011; 23: 4507-4525 * Crossref * PubMed * Scopus (143) * Google Scholar ) with minor alterations. Briefly, the chromatographic separation was performed on an ACQUITY UPLC BEH C18 column (2.1x100 mm, i.d., 1.7 mm) (Waters Corp., MA, USA). The mobile phase A consisted of 95% DDW and 5% acetonitrile, with 0.1% formic acid; mobile phase B consisted of 100% acetonitrile with 0.1% formic acid. The column was maintained at 35@C, and the flow rate of the mobile phase was 0.3 mL^ *min^-1. Mobile phase A was initially run at 100%, and it was gradually reduced to 72% at 22 min, following a decrease to 0% at 36 min. Then, mobile phase B was run at 100% until 38 min; then, mobile phase A was set to 100% at 38.5 min. Finally, the column was equilibrated at 100% mobile phase A until 40 min. MS parameters were as follows: the source and de-solvation temperatures were maintained at 120C and 350C, respectively. The capillary voltage was set to 2 kV at negative ionization mode; cone voltage was set for 40 V. Nitrogen was used as de-solvation gas and cone gas at the flow rate of 700 L^ *h^-1 and 50 L^*h^-1. The mass spectrometer was operated in full scan HDMS^E negative or positive resolution mode over a mass range of 50-2000 Da. For the high-energy scan function, a collision energy ramp of 20-80 eV was applied; for the low energy scan function - 4 eV was applied. Leucine-enkephalin was used as a lock-mass reference standard. Semipolar compounds identification and data processing LC-MS data were analyzed and processed with UNIFI (Version 1.9.4, Waters Corp., MA, USA). The putative identification of the different semipolar species was performed by comparison accurate mass, fragmentation pattern and ion mobility (CCS) values to in-house made semipolar database, when several compounds were identified vs. standards, when available. LC-MS polar metabolite analysis Metabolic profiling of polar phase was done as previously described ( Zheng et al., 20 15 * Zheng L. * Cardaci S. * Jerby L. * MacKenzie E.D. * Sciacovelli M. * Johnson T.I. * Gaude E. * King A. * Leach J.D.G. * Edrada-Ebel R. * et al. Fumarate induces redox-dependent senescence by modifying glutathione metabolism. Nat. Commun. 2015; 6: 6001 * Crossref * PubMed * Google Scholar ) with minor modifications described below. Briefly, analysis was performed using Acquity I class UPLC System combined with mass spectrometer Q Exactive Plus Orbitrap(tm) (Thermo Fisher Scientific) which was operated in a negative ionization mode. The LC separation was done using the SeQuant Zic-pHilic (150 mm x 2.1 mm) with the SeQuant guard column (20 mm x 2.1 mm) (Merck). The Mobile phase B: acetonitrile and Mobile phase A: 20 mM ammonium carbonate with 0.1% ammonia hydroxide in DDW: acetonitrile (80:20, v/v). The flow rate was kept at 200 mL^* min^-1 and gradient as follow: 0-2 min 75% of B, 14 min 25% of B, 18 min 25% of B, 19 min 75% of B, for 4 min, 23 min 75% of B. For metabolites normalization, peak areas of metabolites were divided by summed relative abundances of internal standards (labeled amino acids). Further normalization was carried out by dividing each metabolite with the median value of that metabolite, as it was shown to produce the most accurate results ( Wulff, and Mitchell, 2018 * Wulff J.E. * Mitchell M.W. A comparison of various normalization methods for LC/MS metabolomics data. Adv. Biosci. Biotechnol. 2018; 09: 339-351 * Crossref * Google Scholar ). Statistical tests were employed using repeated measures one way ANOVA for testing of three time points, and by paired t-test for the two time point analysis (for the sucralose and control groups), FDR-BH correction was applied. For pathway analysis P-value significant metabolites of the sucralose group were run in the MetaboAnalyst pipeline ( Pang et al., 2021 * Pang Z. * Chong J. * Zhou G. * de Lima Morais D.A. * Chang L. * Barrette M. * Gauthier C. * Jacques P.E. * Li S. * Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021; 49: W388-W396 * Crossref * PubMed * Scopus (512) * Google Scholar ) using the hypergeometric test and the KEGG (Homo Sapiens) database. Polar metabolites data processing The data processing was done using TraceFinder (Thermo Fisher Scientific), when detected compounds were identified by accurate mass, retention time, isotope pattern, fragments and verified using in-house-generated mass spectra library. Quantification and statistical analysis The sample size for the RCT was calculated to have a power of >80% to detect a 30% increase in glucose tolerance (GTT AUC) with a probability of a type I error (a) of 0.05. This value is based on our preliminary trial, in which we observed an average increase in GTT AUC of 1000 units in saccharin-supplemented individuals ( Suez et al., 2014 * Suez J. * Korem T. * Zeevi D. * Zilberman-Schapira G. * Thaiss C.A. * Maza O. * Israeli D. * Zmora N. * Gilad S. * Weinberger A. * et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 * Crossref * PubMed * Scopus (1108) * Google Scholar ). In all linear mixed-effects models, participants were considered as the random effect, reflected as donors in the mouse modeling. In the mouse modelling, we used either responsiveness (top, bottom) or time point (baseline, day 21) as the response variable, while the bacterial feature (gene, pathway, or bacterial species) was used as the explanatory variable. PERMANOVA was used with the stratification of participants when performing the random permutations. For the correlation analysis, we used the average of all mice relating to the same donor and time point to handle repeated samples. Statistical tests were performed in PRISM (V 9.2), R and Python. The tests used in each analysis are indicated in the main text. Types of center and dispersion measures are indicated in the figure legends. PRISM was used as the primary tool for statistical analysis, apart from the microbiome linear mixed modelling (R, lmer package) and microbiome PERMANOVA and univariate testing (Python). The AUC of log fold change was computed only for the topmost informative features detected by TCAM in primary PCs. The computation was performed by taking the cumulative sum of the log[2] fold change from baseline values of each feature, unrelated to the TCAM algorithm computation. Data integrity check Figure and supplementary figure panels were checked for data integrity using the Proofig pipeline, https://www.proofig.com. Acknowledgments We thank the members of the Elinav lab, Weizmann Institute of Science, and members of the DKFZ microbiome and cancer division for insightful discussions; Carmit Bar-Nathan for dedicated germ-free mouse husbandry; Hadar Ariely, Gili Weinberg, and Dana Regev-Lehavi for coordinating the clinical trial. J.S. is the recipient of the Strauss Institute research fellowship. M.H. is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation , 438122637 ). S.M. and M.I. work was supported by Vera and John Schwartz Family Center for Metabolic Biology . H.S. is the incumbent of the Vera Rosenberg Schwartz Research Fellow Chair. E.E. is supported by The Leona M. and Harry B. Helmsley Charitable Trust ; Adelis Foundation ; Ben B. and Joyce E. Eisenberg Foundation ; Estate of Bernard Bishin for the WIS-Clalit Program ; Jeanne and Joseph Nissim Center for Life Sciences Research ; Miel de Botton ; Swiss Society Institute for Cancer Prevention Research ; Belle S. and Irving E. Meller Center for the Biology of Aging; Sagol Institute for Longevity Research ; Sagol Weizmann-MIT Bridge Program ; Norman E Alexander Family M Foundation Coronavirus Research Fund ; Mike and Valeria Rosenbloom Foundation ; Daniel Morris Trust ; Isidore and Penny Myers Foundation ; Vainboim Family ; and by grants funded by the European Research Council ; Israel Science Foundation ; Israel Ministry of Science and Technology ; Israel Ministry of Health ; the German-Israeli Helmholtz International Research School : cancer-TRAX ( HIRS-0003 ); Helmholtz Association's Initiative and Networking Fund ; Minerva Foundation ; Garvan Institute ; European Crohn's and Colitis Organization ; Deutsch-Israelische Projektkooperation ; IDSA Foundation ; WIS-MIT grant ; Emulate ; Charlie Teo Foundation ; Mark Foundation for Cancer Research , and Wellcome Trust . E.E. is the incumbent of the Sir Marc and Lady Tania Feldmann Professorial chair of Immunology; a senior fellow, Canadian Institute of Advanced Research (CIFAR); and an international scholar, the Bill & Melinda Gates Foundation and Howard Hughes Medical Institute (HHMI). Author contributions J.S. and E.E. conceived the study and designed the intervention; directed the human trial and data collection; designed, performed, analyzed, and interpreted experiments and computational analysis; and wrote the manuscript. Y.C. headed and performed all computational analyses, analyzed and interpreted the results, wrote the manuscript, and equally contributed to the study. R.V.-M. and U.M. performed computational analyses and provided essential tools and insights. M.D.-B. performed sample processing and next-generation DNA sequencing. S.F., H.S., A.L., R.L., and M.H. performed and assisted in experiments and sample processing. N.Z. participated in study design and protocol development. A.B., M.Z., R.B.-Z.B., S.E.-M., A.M., R.F., and O.S. coordinated the randomized-controlled trial, including data and sample collection; and recruiting, training, and following up on participants. S.M. and M.I. performed metabolomics experiments. N.S. and A.H. supervised all GF experiments. C.K.S.-T. contributed key insights and tools. E.S. and E.E. co-supervised the study. Declaration of interests E.S. is a scientific co-founder of DayTwo. E.E. is a scientific co-founder of DayTwo and BiomX, and a paid consultant to Hello Inside and Aposense. E.E. is a member of the Cell scientific advisory board. Supplemental information * Download .xlsx (.02 MB) Help with xlsx files Table S1. Baseline characteristics of the cohort by group, related to Figure 1 Values are means +- SDs or medians (IQRs) unless otherwise indicated. BPM, beats per minute; DBP, diastolic blood pressure; SBP, systolic blood pressure. * Download .xlsx (.03 MB) Help with xlsx files Table S2. Anthropometrics and blood test statistical analysis, related to Figure 2 Values are q-values following FDR-correction. * Download .xlsx (.06 MB) Help with xlsx files Table S3. Glycemic response statistics with sensitivity analysis, related to Figure 2 Results of linear mixed modeling of GTT performed in humans considering age, gender, BMI and smoking status. * Download .xlsx (.01 MB) Help with xlsx files Table S4. Stool and oral baseline shotgun metagenomic, related to Figure 3 PCA and PERMANOVA analysis of stool and oral microbiome samples at baseline. * Download .xlsx (1.81 MB) Help with xlsx files Table S5. Stool and oral shotgun metagenomic and serum metabolomic analysis - saccharin, stevia, and aspartame, related to Figures 4 and 5 Analysis of stool and oral microbiome samples of the saccharin, stevia, and aspartame consuming groups. Analysis of blood metabolomics of the saccharin, stevia, and aspartame consuming groups and respective glucose vehicle and NSC control groups. * Download .xlsx (1.79 MB) Help with xlsx files Table S6. Stool and oral shotgun metagenomic and serum metabolomic analysis - sucralose, NSC and glucose vehicle, related to Figures 4 and 5 Analysis of stool and oral microbiome samples of the sucralose consuming group, NSC and glucose vehicle groups. Analysis of blood metabolomics of the sucralose consuming group and respective glucose vehicle and NSC control groups. * Download .xlsx (.08 MB) Help with xlsx files Table S7. Stool shotgun metagenomics in mice, related to Figure 7 Analysis of stool microbiome samples of the germ-free mice transplant experiment. References 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 A. 2008; 71: 1415-1429 View in Article + Scopus (210) + PubMed + Crossref + Google Scholar 2. + Ahmad S.Y. + Friel J. + Mackay D. The effects of non-nutritive artificial sweeteners, aspartame and sucralose, on the gut microbiome in healthy adults: secondary outcomes of a randomized double-blinded crossover clinical trial. Nutrients. 2020; 12: 3408 View in Article + Scopus (12) + Crossref + Google Scholar 3. + Ahmad S.Y. + Friel J.K. + MacKay D.S. The effect of the artificial sweeteners on glucose metabolism in healthy adults: a randomized, double-blinded, crossover clinical trial. Appl. Physiol. Nutr. Metab. 2020; 45: 606-612 View in Article + Scopus (13) + PubMed + Crossref + Google Scholar 4. + Ananthakrishnan A.N. + Luo C. + Yajnik V. + Khalili H. + Garber J.J. + Stevens B.W. + Cleland T. + Xavier R.J. Gut microbiome function predicts response to anti-integrin biologic therapy in inflammatory bowel diseases. Cell Host Microbe. 2017; 21: 603-610.e3 View in Article + Scopus (180) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 5. + Anderson R.L. + Kirkland J.J. The effect of sodium saccharin in the diet on caecal microflora. Food Cosmet. Toxicol. 1980; 18: 353-355 View in Article + PubMed + Crossref + Google Scholar 6. + Azad M.B. + Abou-Setta A.M. + Chauhan B.F. + Rabbani R. + Lys J. + Copstein L. + Mann A. + Jeyaraman M.M. + Reid A.E. + Fiander M. + et al. Chronic sucralose consumpt. CMAJ. 2017; 189: E929-E939 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 7. + Azad M.B. + Archibald A. + Tomczyk M.M. + Head A. + Cheung K.G. + de Souza R.J. + Becker A.B. + Mandhane P.J. + Turvey S.E. + Moraes T.J. + et al. Nonnutritive sweetener consumption during pregnancy, adiposity, and adipocyte differentiation in offspring: evidence from humans, mice, and cells. Int. J. Obes. (Lond). 2020; 44: 2137-2148 View in Article + Scopus (10) + PubMed + Crossref + Google Scholar 8. + Bailey C.J. + Day C. + Knapper J.M. + Turner S.L. + Flatt P.R. Antihyperglycaemic effect of saccharin in diabetic ob/ob mice. Br. J. Pharmacol. 1997; 120: 74-78 View in Article + Scopus (23) + PubMed + Crossref + Google Scholar 9. + Bailey T.S. + Ahmann A. + Brazg R. + Christiansen M. + Garg S. + Watkins E. + Welsh J.B. + Lee S.W. Accuracy and acceptability of the 6-day Enlite continuous subcutaneous glucose sensor. Diabetes Technol. Ther. 2014; 16: 277-283 View in Article + Scopus (65) + PubMed + Crossref + Google Scholar 10. + Ball L.M. + Renwick A.G. + Williams R.T. The fate of [14C]saccharin in rats chronically fed on saccharin. Biochem. Soc. Trans. 1974; 2: 1084-1086 View in Article + Scopus (4) + Crossref + Google Scholar 11. + Balvers M. + Deschasaux M. + van den Born B.J. + Zwinderman K. + Nieuwdorp M. + Levin E. Analyzing type 2 diabetes associations with the gut microbiome in individuals from two ethnic backgrounds living in the same geographic area. Nutrients. 2021; 13: 3289 View in Article + Scopus (2) + PubMed + Crossref + Google Scholar 12. + Berry S.E. + Valdes A.M. + Drew D.A. + Asnicar F. + Mazidi M. + Wolf J. + Capdevila J. + Hadjigeorgiou G. + Davies R. + Al Khatib H. + et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 2020; 26: 964-973 View in Article + Scopus (153) + PubMed + Crossref + Google Scholar 13. + Bian X. + Chi L. + Gao B. + Tu P. + Ru H. + Lu K. The artificial sweetener acesulfame potassium affects the gut microbiome and body weight gain in CD-1 mice. PLoS One. 2017; 12: e0178426 View in Article + Scopus (100) + PubMed + Crossref + Google Scholar 14. + Bian X. + Chi L. + Gao B. + Tu P. + Ru H. + Lu K. Gut microbiome response to sucralose and its potential role in inducing liver inflammation in mice. Front. Physiol. 2017; 8: 487https://doi.org/10.3389/ fphys.2017.00487 View in Article + Scopus (114) + PubMed + Crossref + Google Scholar 15. + Bian X. + Tu P. + Chi L. + Gao B. + Ru H. + Lu K. Saccharin induced liver inflammation in mice by altering the gut microbiota and its metabolic functions. Food Chem. Toxicol. 2017; 107: 530-539 View in Article + Scopus (81) + PubMed + Crossref + Google Scholar 16. + Blackburn G.L. + Kanders B.S. + Lavin P.T. + Keller S.D. + Whatley J. The effect of aspartame as part of a multidisciplinary weight-control program on short- and long-term control of body weight. Am. J. Clin. Nutr. 1997; 65: 409-418 View in Article + Scopus (155) + PubMed + Crossref + Google Scholar 17. + Bolger A.M. + Lohse M. + Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30: 2114-2120 View in Article + Scopus (24587) + PubMed + Crossref + Google Scholar 18. + Bornemann V. + Werness S.C. + Buslinger L. + Schiffman S.S. Intestinal metabolism and bioaccumulation of sucralose in adipose tissue in the rat. J. Toxicol. Environ. Health A. 2018; 81: 913-923 View in Article + Scopus (14) + PubMed + Crossref + Google Scholar 19. + Buchfink B. + Reuter K. + Drost H.G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods. 2021; 18: 366-368 View in Article + Scopus (122) + PubMed + Crossref + Google Scholar 20. + Bueno-Hernandez N. + Esquivel-Velazquez M. + Alcantara-Suarez R. + Gomez-Arauz A.Y. + Espinosa-Flores A.J. + de Leon-Barrera K.L. + Mendoza-Martinez V.M. + Sanchez Medina G.A. + Leon-Hernandez M. + Ruiz-Barranco A. + et al. Chronic sucralose consumption induces elevation of serum insulin in young healthy adults: a randomized, double blind, controlled trial. Nutr. J. 2020; 19: 32 View in Article + Scopus (8) + PubMed + Crossref + Google Scholar 21. + Cani P.D. + Depommier C. + Derrien M. + Everard A. + de Vos W.M. Akkermansia muciniphila: paradigm for next-generation beneficial microorganisms. Nature Reviews Gastroenterology & Hepatology. 2022; : 1-13 View in Article + PubMed + Google Scholar 22. + Caspi R. + Billington R. + Fulcher C.A. + Keseler I.M. + Kothari A. + Krummenacker M. + Latendresse M. + Midford P.E. + Ong Q. + Ong W.K. + et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018; 46: D633-D639 View in Article + Scopus (378) + PubMed + Crossref + Google Scholar 23. + Chassaing B. + Koren O. + Goodrich J.K. + Poole A.C. + Srinivasan S. + Ley R.E. + Gewirtz A.T. Dietary emulsifiers impact the mouse gut microbiota promoting colitis and metabolic syndrome. Nature. 2015; 519: 92-96 View in Article + Scopus (998) + PubMed + Crossref + Google Scholar 24. + Cheng X. + Guo X. + Huang F. + Lei H. + Zhou Q. + Song C. Effect of different sweeteners on the oral microbiota and immune system of Sprague Dawley rats. AMB Express. 2021; 11: 8 View in Article + Scopus (1) + PubMed + Crossref + Google Scholar 25. + Chi L. + Bian X. + Gao B. + Tu P. + Lai Y. + Ru H. + Lu K. Effects of the artificial sweetener neotame on the gut microbiome and fecal metabolites in mice. Molecules. 2018; 23: 367 View in Article + Scopus (40) + Crossref + Google Scholar 26. + Collison K.S. + Makhoul N.J. + Zaidi M.Z. + Saleh S.M. + Andres B. + Inglis A. + Al-Rabiah R. + Al-Mohanna F.A. Gender dimorphism in aspartame-induced impairment of spatial cognition and insulin sensitivity. PLoS One. 2012; 7: e31570 View in Article + Scopus (37) + PubMed + Crossref + Google Scholar 27. + Dai X. + Wang C. + Guo Z. + Li Y. + Liu T. + Jin G. + Wang S. + Wang B. + Jiang K. + Cao H. Maternal sucralose exposure induces Paneth cell defects and exacerbates gut dysbiosis of progeny mice. Food Funct. 2021; 12: 12634-12646 View in Article + PubMed + Crossref + Google Scholar 28. + Dalenberg J.R. + Patel B.P. + Denis R. + Veldhuizen M.G. + Nakamura Y. + Vinke P.C. + Luquet S. + Small D.M. Short-term consumption of sucralose with, but not without, carbohydrate impairs neural and metabolic sensitivity to sugar in humans. Cell Metab. 2020; 31: 493-502.e7 View in Article + Scopus (50) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 29. + Daly K. + Darby A.C. + Hall N. + Nau A. + Bravo D. + Shirazi-Beechey S.P. Dietary supplementation with lactose or artificial sweetener enhances swine gut Lactobacillus population abundance. Br. J. Nutr. 2014; 111: S30-S35 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 30. + Dash N.R. + Al Bataineh M.T. Metagenomic analysis of the gut microbiome reveals enrichment of menaquinones (vitamin K2) pathway in diabetes mellitus. Diabetes Metab. J. 2021; 45: 77-85 View in Article + Scopus (8) + PubMed + Crossref + Google Scholar 31. + Ebbeling C.B. + Feldman H.A. + Steltz S.K. + Quinn N.L. + Robinson L.M. + Ludwig D.S. Effects of sugar-sweetened, artificially sweetened, and unsweetened beverages on cardiometabolic risk factors, body composition, and sweet taste preference: a randomized controlled trial. J. Am. Heart Assoc. 2020; 9: e015668 View in Article + Scopus (13) + PubMed + Crossref + Google Scholar 32. + Sarwar N. + Gao P. + Seshasai S.R.K. + Gobin R. + Kaptoge S. + Di Angelantonio E. + Ingelsson E. + Lawlor D.A. + Selvin E. + et al. + Emerging Risk Factors Collaboration Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010; 375: 2215-2222 View in Article + Scopus (2836) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 33. + Fan Y. + Pedersen O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 2021; 19: 55-71 View in Article + Scopus (560) + PubMed + Crossref + Google Scholar 34. + Feijo F.M. + Ballard C.R. + Foletto K.C. + Batista B.A.M. + Neves A.M. + Ribeiro M.F.M. + Bertoluci M.C. Saccharin and aspartame, compared with sucrose, induce greater weight gain in adult Wistar rats, at similar total caloric intake levels. Appetite. 2013; 60: 203-207 View in Article + Scopus (73) + PubMed + Crossref + Google Scholar 35. + Fernandez-Garcia J.C. + Delpino-Rius A. + Samarra I. + Castellano-Castillo D. + Munoz-Garach A. + Bernal-Lopez M.R. + Queipo-Ortuno M.I. + Cardona F. + Ramos-Molina B. + Tinahones F.J. Type 2 diabetes is associated with a different pattern of serum polyamines: a Case^-Control study from the PREDIMED-Plus trial. J. Clin. Med. 2019; 8: 71 View in Article + PubMed + Crossref + Google Scholar 36. + Fiehn O. + Garvey W.T. + Newman J.W. + Lok K.H. + Hoppel C.L. + Adams S.H. Plasma metabolomic profiles reflective of glucose homeostasis in non-diabetic and type 2 diabetic obese African-American women. PLoS One. 2010; 5: e15234 View in Article + Scopus (301) + PubMed + Crossref + Google Scholar 37. + Frankenfeld C.L. + Sikaroodi M. + Lamb E. + Shoemaker S. + Gillevet P.M. High-intensity sweetener consumption and gut microbiome content and predicted gene function in a cross-sectional study of adults in the United States. Ann. Epidemiol. 2015; 25 (42.e4): 736 View in Article + PubMed + Crossref + Google Scholar 38. + Franzosa E.A. + McIver L.J. + Rahnavard G. + Thompson L.R. + Schirmer M. + Weingart G. + Lipson K.S. + Knight R. + Caporaso J.G. + Segata N. + Huttenhower C. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods. 2018; 15: 962-968 View in Article + Scopus (580) + PubMed + Crossref + Google Scholar 39. + Gardana C. + Simonetti P. + Canzi E. + Zanchi R. + Pietta P. Metabolism of stevioside and rebaudioside A from stevia rebaudiana extracts by human microflora. J. Agric. Food Chem. 2003; 51: 6618-6622 View in Article + Scopus (159) + PubMed + Crossref + Google Scholar 40. + Gardner C. + Wylie-Rosett J. + Gidding S.S. + Steffen L.M. + Johnson R.K. + Reader D. + Lichtenstein A.H. Nonnutritive sweeteners: current use and health perspectives: a scientific statement from the American Heart Association and the American Diabetes Association. Diabetes Care. 2012; 126: 1798-1808 View in Article + Scopus (157) + Crossref + Google Scholar 41. + Gopalakrishnan V. + Spencer C.N. + Nezi L. + Reuben A. + Andrews M.C. + Karpinets T.V. + Prieto P.A. + Vicente D. + Hoffman K. + Wei S.C. + et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018; 359: 97-103 View in Article + Scopus (1931) + PubMed + Crossref + Google Scholar 42. + Guasch-Ferre M. + Santos J.L. + Martinez-Gonzalez M.A. + Clish C.B. + Razquin C. + Wang D. + Liang L. + Li J. + Dennis C. + Corella D. + et al. Glycolysis/gluconeogenesis- and tricarboxylic acid cycle-related metabolites, Mediterranean diet, and type 2 diabetes. Am. J. Clin. Nutr. 2020; 111: 835-844 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 43. + Gul S.S. + Hamilton A.R.L. + Munoz A.R. + Phupitakphol T. + Liu W. + Hyoju S.K. + Economopoulos K.P. + Morrison S. + Hu D. + Zhang W. + et al. Inhibition of the gut enzyme intestinal alkaline phosphatase may explain how aspartame promotes glucose intolerance and obesity in mice. Appl. Physiol. Nutr. Metab. 2017; 42: 77-83 View in Article + Scopus (28) + PubMed + Crossref + Google Scholar 44. + Guo M. + Liu X. + Tan Y. + Kang F. + Zhu X. + Fan X. + Wang C. + Wang R. + Liu Y. + Qin X. + et al. Sucralose enhances the susceptibility to dextran sulfate sodium (DSS) induced colitis in mice with changes in gut microbiota. Food Funct. 2021; 12: 9380-9390 View in Article + PubMed + Crossref + Google Scholar 45. + Hanawa Y. + Higashiyama M. + Kurihara C. + Tanemoto R. + Ito S. + Mizoguchi A. + Nishii S. + Wada A. + Inaba K. + Sugihara N. + et al. Acesulfame potassium induces dysbiosis and intestinal injury with enhanced lymphocyte migration to intestinal mucosa. J. Gastroenterol. Hepatol. 2021; 36: 3140-3148 View in Article + Scopus (1) + PubMed + Crossref + Google Scholar 46. + Harpaz D. + Yeo L.P. + Cecchini F. + Koon T.H.P. + Kushmaro A. + Tok A.I.Y. + Marks R.S. + Eltzov E. Measuring artificial sweeteners toxicity using a bioluminescent bacterial panel. Molecules. 2018; 23: 2454 View in Article + Scopus (32) + Crossref + Google Scholar 47. + Harrington V. + Lau L. + Crits-Christoph A. + Suez J. Interactions of non-nutritive artificial sweeteners with the microbiome in metabolic syndrome. Immunometabolism. 2022; 4: e220012 View in Article + PubMed + Crossref + Google Scholar 48. + He Z. + Chen L. + Catalan-Dibene J. + Bongers G. + Faith J.J. + Suebsuwong C. + DeVita R.J. + Shen Z. + Fox J.G. + Lafaille J.J. + Lira S.A. Food colorants metabolized by commensal bacteria promote colitis in mice with dysregulated expression of interleukin-23. Cell Metab. 2021; 33: 1358-1371.e5 View in Article + Scopus (10) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 49. + Higgins K.A. + Mattes R.D. A randomized controlled trial contrasting the effects of 4 low-calorie sweeteners and sucrose on body weight in adults with overweight or obesity. Am. J. Clin. Nutr. 2019; 109: 1288-1301 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 50. + Hu F.B. Resolved: there is sufficient scientific evidence that decreasing sugar-sweetened beverage consumption will reduce the prevalence of obesity and obesity-related diseases. Obes. Rev. 2013; 14: 606-619 View in Article + Scopus (611) + PubMed + Crossref + Google Scholar 51. + Imes C.C. + Burke L.E. The obesity epidemic: the United States as a cautionary tale for the rest of the world. Curr. Epidemiol. Rep. 2014; 1: 82-88 View in Article + PubMed + Crossref + Google Scholar 52. + Itkin M. + Rogachev I. + Alkan N. + Rosenberg T. + Malitsky S. + Masini L. + Meir S. + Iijima Y. + Aoki K. + de Vos R. + et al. Glycoalkaloid METABOLISM1 is required for steroidal alkaloid glycosylation and prevention of phytotoxicity in tomato. Plant Cell. 2011; 23: 4507-4525 View in Article + Scopus (143) + PubMed + Crossref + Google Scholar 53. + John B.A. + Wood S.G. + Hawkins D.R. The pharmacokinetics and metabolism of sucralose in the mouse. Food Chem. Toxicol. 2000; 38: S107-S110 View in Article + PubMed + Crossref + Google Scholar 54. + Johnson R.K. + Lichtenstein A.H. + Anderson C.A.M. + Carson J.A. + Despres J.P. + Hu F.B. + Kris-Etherton P.M. + Otten J.J. + Towfighi A. + Wylie-Rosett J. + et al. Low-calorie sweetened beverages and cardiometabolic health: A science advisory from the American Heart Association. Circulation. 2018; 138: e126-e140https://doi.org/10.1161/ CIR.0000000000000569 View in Article + Scopus (73) + PubMed + Crossref + Google Scholar 55. + Kanehisa M. + Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000; 28: 27-30 View in Article + PubMed + Crossref + Google Scholar 56. + Katan M.B. + de Ruyter J.C. + Kuijper L.D.J. + Chow C.C. + Hall K.D. + Olthof M.R. Impact of masked replacement of sugar-sweetened with sugar-free beverages on body weight increases with initial BMI: secondary analysis of data from an 18 month double-blind trial in children. PLoS One. 2016; 11: e0159771 View in Article + Scopus (21) + PubMed + Crossref + Google Scholar 57. + Katzmarzyk P.T. + Broyles S.T. + Champagne C.M. + Chaput J.P. + Fogelholm M. + Hu G. + Kuriyan R. + Kurpad A. + Lambert E.V. + Maia J. + et al. Relationship between soft drink consumption and obesity in 9-11 years old children in a multi-national study. Nutrients. 2016; 8: 770 View in Article + Scopus (30) + Crossref + Google Scholar 58. + Kilmer M.E. + Horesh L. + Avron H. + Newman E. Tensor-tensor algebra for optimal representation and compression of multiway data. Proc. Natl. Acad. Sci. USA. 2021; 118 (e2015851118) View in Article + Scopus (7) + PubMed + Crossref + Google Scholar 59. + Kim Y. + Keogh J.B. + Clifton P.M. Consumption of a beverage containing aspartame and acesulfame K for two weeks does not adversely influence glucose metabolism in adult males and females: A randomized crossover study. Int. J. Environ. Res. Public Health. 2020; 17: 9049 View in Article + Scopus (0) + Crossref + Google Scholar 60. + Korem T. + Zeevi D. + Zmora N. + Weissbrod O. + Bar N. + Lotan-Pompan M. + Avnit-Sagi T. + Kosower N. + Malka G. + Rein M. + et al. Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 2017; 25: 1243-1253.e5 View in Article + Scopus (143) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 61. + Kovatcheva-Datchary P. + Nilsson A. + Akrami R. + Lee Y.S. + De Vadder F. + Arora T. + Hallen A. + Martens E. + Bjorck I. + Backhed F. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 2015; 22: 971-982 View in Article + Scopus (762) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 62. + Labare M.P. + Alexander M. Microbial cometabolism of sucralose, a chlorinated disaccharide, in environmental samples. Appl. Microbiol. Biotechnol. 1994; 42: 173-178 View in Article + Scopus (38) + PubMed + Crossref + Google Scholar 63. + Laforest-Lapointe I. + Becker A.B. + Mandhane P.J. + Turvey S.E. + Moraes T.J. + Sears M.R. + Subbarao P. + Sycuro L.K. + Azad M.B. + Arrieta M.-C. Maternal consumption of artificially sweetened beverages during pregnancy is associated with infant gut microbiota and metabolic modifications and increased infant body mass index. Gut Microbes. 2021; 13: 1-15 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 64. + Leibowitz A. + Bier A. + Gilboa M. + Peleg E. + Barshack I. + Grossman E. Saccharin increases fasting blood glucose but not liver insulin resistance in comparison to a high fructose-fed rat model. Nutrients. 2018; 10: 341 View in Article + Scopus (9) + Crossref + Google Scholar 65. + Lertrit A. + Srimachai S. + Saetung S. + Chanprasertyothin S. + Chailurkit L.O. + Areevut C. + Katekao P. + Ongphiphadhanakul B. + Sriphrapradang C. Effects of sucralose on insulin and glucagon-like peptide-1 secretion in healthy subjects: a randomized, double-blind, placebo-controlled trial. Nutrition. 2018; 55-56: 125-130 View in Article + Scopus (42) + PubMed + Crossref + Google Scholar 66. + Li J. + Jia H. + Cai X. + Zhong H. + Feng Q. + Sunagawa S. + Arumugam M. + Kultima J.R. + Prifti E. + Nielsen T. + et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 2014; 32: 834-841 View in Article + Scopus (1036) + PubMed + Crossref + Google Scholar 67. + Li J. + Zhu S. + Lv Z. + Dai H. + Wang Z. + Wei Q. + Hamdard E. + Mustafa S. + Shi F. + Fu Y. Drinking water with saccharin sodium alters the microbiota-gut-hypothalamus axis in guinea pig. Animals (Basel). 2021; 11: 1875 View in Article + Scopus (2) + PubMed + Crossref + Google Scholar 68. + Liu C. + Wang Y. + Zheng W. + Wang J. + Zhang Y. + Song W. + Wang A. + Ma X. + Li G. Putrescine as a novel biomarker of maternal serum in first trimester for the prediction of gestational diabetes mellitus: A nested case-control study. Front. Endocrinol. (Lausanne). 2021; 12: 759893https://doi.org/ 10.3389/fendo.2021.759893 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 69. + Lohner S. + Kuellenberg de Gaudry D. + Toews I. + Ferenci T. + Meerpohl J.J. Non-nutritive sweeteners for diabetes mellitus. Cochrane Database Syst. Rev. 2020; 5: CD012885 View in Article + PubMed + Google Scholar 70. + Lu J. + Breitwieser F.P. + Thielen P. + Salzberg S.L. Bracken: estimating species abundance in metagenomics data. PeerJ Comput. Sci. 2017; 3: e104 View in Article + Scopus (314) + Crossref + Google Scholar 71. + Lyte M. + Fodor A.A. + Chapman C.D. + Martin G.G. + Perez-Chanona E. + Jobin C. + Dess N.K. Gut microbiota and a selectively bred taste phenotype: A novel model of microbiome-behavior relationships. Psychosom. Med. 2016; 78: 610-619 View in Article + Scopus (18) + PubMed + Crossref + Google Scholar 72. + Magnuson B.A. + Carakostas M.C. + Moore N.H. + Poulos S.P. + Renwick A.G. Biological fate of low-calorie sweeteners. Nutr. Rev. 2016; 74: 670-689 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 73. + Malik V.S. + Hu F.B. The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nature Reviews Endocrinology. 2022; 18: 205-218 View in Article + Scopus (14) + PubMed + Crossref + Google Scholar 74. + Malitsky S. + Ziv C. + Rosenwasser S. + Zheng S. + Schatz D. + Porat Z. + Ben-Dor S. + Aharoni A. + Vardi A. Viral infection of the marine alga Emiliania huxleyi triggers lipidome remodeling and induces the production of highly saturated triacylglycerol. New Phytol. 2016; 210: 88-96 View in Article + PubMed + Crossref + Google Scholar 75. + Manor O. + Borenstein E. Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome. Microbiome. 2017; 5: 19https://doi.org/10.1186/s40168-017-0231-4 View in Article + Scopus (14) + PubMed + Crossref + Google Scholar 76. + Markus V. + Share O. + Shagan M. + Halpern B. + Bar T. + Kramarsky-Winter E. + Terali K. + Ozer N. + Marks R.S. + Kushmaro A. + Golberg K. Inhibitory effects of artificial sweeteners on bacterial quorum sensing. Int. J. Mol. Sci. 2021; 22: 9863 View in Article + Scopus (1) + PubMed + Crossref + Google Scholar 77. + Martinez X. + Zapata Y. + Pinto V. + Cornejo C. + Elbers M. + van der Graaf M.V. + Villarroel L. + Hodgson M.I. + Rigotti A. + Echeverria G. Intake of non-nutritive sweeteners in Chilean children after enforcement of a new food labeling law that regulates added sugar content in processed foods. Nutrients. 2020; 12: 1594 View in Article + Scopus (14) + Crossref + Google Scholar 78. + Martinez-Carrillo B.E. + Rosales-Gomez C.A. + Ramirez-Duran N. + Resendiz-Albor A.A. + Escoto-Herrera J.A. + Mondragon-Velasquez T. + Valdes-Ramos R. + Castillo-Cardiel A. Effect of chronic consumption of sweeteners on microbiota and immunity in the small intestine of young mice. Int. J. Food Sci. 2019; 2019: 9619020 View in Article + Scopus (9) + PubMed + Crossref + Google Scholar 79. + Masic U. + Harrold J.A. + Christiansen P. + Cuthbertson D.J. + Hardman C.A. + Robinson E. + Halford J.C.G. Effects of non-nutritive sWeetened beverages on appetITe during aCtive weigHt loss (SWITCH): protocol for a randomized, controlled trial assessing the effects of non-nutritive sweetened beverages compared to water during a 12-week weight loss period and a follow up weight maintenance period. Contemp. Clin. Trials. 2017; 53: 80-88 View in Article + Scopus (0) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 80. + Matson V. + Fessler J. + Bao R. + Chongsuwat T. + Zha Y. + Alegre M.L. + Luke J.J. + Gajewski T.F. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science. 2018; 359: 104-108 View in Article + Scopus (1239) + PubMed + Crossref + Google Scholar 81. + Mendez-Garcia L.A. + Bueno-Hernandez N. + Cid-Soto M.A. + De Leon K.L. + Mendoza-Martinez V.M. + Espinosa-Flores A.J. + Carrero-Aguirre M. + Esquivel-Velazquez M. + Leon-Hernandez M. + Viurcos-Sanabria R. + et al. Ten-week sucralose consumption induces gut dysbiosis and altered glucose and insulin levels in healthy young adults. Microorganisms. 2022; 10: 434 View in Article + Scopus (1) + PubMed + Crossref + Google Scholar 82. + Meric G. + Wick R.R. + Watts S.C. + Holt K.E. + Inouye M. Correcting index databases improves metagenomic studies. bioRxiv. 2019; https://doi.org/10.1101/712166 View in Article + Scopus (0) + Crossref + Google Scholar 83. + Miller P.E. + 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; 100: 765-777 View in Article + Scopus (189) + PubMed + Crossref + Google Scholar 84. + Mitsutomi K. + Masaki T. + Shimasaki T. + Gotoh K. + Chiba S. + Kakuma T. + Shibata H. Effects of a nonnutritive sweetener on body adiposity and energy metabolism in mice with diet-induced obesity. Metabolism. 2014; 63: 69-78 View in Article + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 85. + Mor U. + Cohen Y. + Valdes-Mas R. + Kviatcovsky D. + Elinav E. + Avrom H. Dimensionality reduction of longitudinal 'omics data using modern tensor factorizations. PLoS Comput Biol. 2022; 18https://doi.org/10.1371/ journal.pcbi.1010212 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 86. + Naim M. + Zechman J.M. + Brand J.G. + Kare M.R. + Sandovsky V. Effects of sodium saccharin on the activity of trypsin, chymotrypsin, and amylase and upon bacteria in small intestinal contents of rats. Proc. Soc. Exp. Biol. Med. 1985; 178: 392-401 View in Article + PubMed + Crossref + Google Scholar 87. + NCD Risk Factor Collaboration (NCD-RisC) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128*9 million children, adolescents, and adults. Lancet. 2017; 380: 2627-2642https://doi.org/10.1016/S0140-6736 (17)32129-3 View in Article + Scopus (3343) + Abstract + Full Text + Full Text PDF + Google Scholar 88. + Nettleton J.E. + Cho N.A. + Klancic T. + Nicolucci A.C. + Shearer J. + Borgland S.L. + Johnston L.A. + Ramay H.R. + Noye Tuplin E. + Chleilat F. + et al. Maternal low-dose aspartame and stevia consumption with an obesogenic diet alters metabolism, gut microbiota and mesolimbic reward system in rat dams and their offspring. Gut. 2020; 69: 1807-1817 View in Article + Scopus (22) + PubMed + Crossref + Google Scholar 89. + Nettleton J.E. + Klancic T. + Schick A. + Choo A.C. + Shearer J. + Borgland S.L. + Chleilat F. + Mayengbam S. + Reimer R.A. Low-dose stevia (rebaudioside A) consumption perturbs gut microbiota and the mesolimbic dopamine reward system. Nutrients. 2019; 11: 1248 View in Article + Scopus (32) + Crossref + Google Scholar 90. + Nichol A.D. + Salame C. + Rother K.I. + Pepino M.Y. Effects of sucralose ingestion versus sucralose taste on metabolic responses to an oral glucose tolerance test in participants with normal weight and obesity: A randomized crossover trial. Nutrients. 2019; 12: 29 View in Article + Scopus (8) + Crossref + Google Scholar 91. + Olivier-Van Stichelen S. + Rother K.I. + Hanover J.A. Maternal exposure to non-nutritive sweeteners impacts progeny's metabolism and microbiome. Front. Microbiol. 2019; 10: 1360 View in Article + Scopus (39) + PubMed + Crossref + Google Scholar 92. + Omran A. + Ahearn G. + Bowers D. + Swenson J. + Coughlin C. Metabolic effects of sucralose on environmental bacteria. J. Toxicol. 2013; 2013: 372986 View in Article + Scopus (23) + PubMed + Crossref + Google Scholar 93. + Otero-Losada M. + Cao G. + Mc Loughlin S. + Rodriguez-Granillo G. + Ottaviano G. + Milei J. Rate of atherosclerosis progression in ApoE-/- mice long after discontinuation of cola beverage drinking. PLoS One. 2014; 9: e89838 View in Article + Scopus (8) + PubMed + Crossref + Google Scholar 94. + Palmnas M.S.A. + Cowan T.E. + Bomhof M.R. + Su J. + Reimer R.A. + Vogel H.J. + Hittel D.S. + Shearer J. Low-dose aspartame consumption differentially affects gut microbiota-host metabolic interactions in the diet-induced obese rat. PLoS One. 2014; 9: e109841 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 95. + Pang Z. + Chong J. + Zhou G. + de Lima Morais D.A. + Chang L. + Barrette M. + Gauthier C. + Jacques P.E. + Li S. + Xia J. MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res. 2021; 49: W388-W396 View in Article + Scopus (512) + PubMed + Crossref + Google Scholar 96. + Parlee S.D. + Simon B.R. + Scheller E.L. + Alejandro E.U. + Learman B.S. + Krishnan V. + Bernal-Mizrachi E. + MacDougald O.A. Administration of saccharin to neonatal mice influences body composition of adult males and reduces body weight of females. Endocrinology. 2014; 155: 1313-1326 View in Article + Scopus (16) + PubMed + Crossref + Google Scholar 97. + Pfeffer M. + Ziesenitz S.C. + Siebert G. Acesulfame K, cyclamate and saccharin inhibit the anaerobic fermentation of glucose by intestinal bacteria. Z. Ernahrungswiss. 1985; 24: 231-235 View in Article + Scopus (27) + PubMed + Crossref + Google Scholar 98. + Prashant G.M. + Patil R.B. + Nagaraj T. + Patel V.B. The antimicrobial activity of the three commercially available intense sweeteners against common periodontal pathogens: an in vitro study. J. Contemp. Dent. Pract. 2012; 13: 749-752 View in Article + Scopus (24) + PubMed + Crossref + Google Scholar 99. + Qu Y. + Li R. + Jiang M. + Wang X. Sucralose increases antimicrobial resistance and stimulates recovery of Escherichia coli mutants. Curr. Microbiol. 2017; 74: 885-888 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 100. + Renwick A.G. The disposition of saccharin in animals and man--a review. Food Chem. Toxicol. 1985; 23: 429-435 View in Article + Scopus (67) + PubMed + Crossref + Google Scholar 101. + Rettig S. + Tenewitz J. + Ahearn G. + Coughlin C. Sucralose causes a concentration dependent metabolic inhibition of the gut flora Bacteroides, B. fragilis and B. uniformis not observed in the Firmicutes, E. faecalis and C. sordellii (1118.1). FASEB J. 2014; 28https://doi.org/10.1096/ fasebj.28.1_supplement.1118.1 View in Article + Crossref + Google Scholar 102. + Risdon S. + Paillargue M. + Meyer G. + Walther G. Non-nutritive sweetener sucralose chronic consumption is able to reduce the deleterious effect of high-fat diet on body composition, glucose metabolism and vascular function in C57BL/ 6JR mice. Arch. Cardiovasc. Dis. Suppl. 2020; 12: 208 View in Article + Google Scholar 103. + Roberts A. + Renwick A.G. + Sims J. + Snodin D.J. Sucralose metabolism and pharmacokinetics in man. Food Chem. Toxicol. 2000; 38: S31-S41 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 104. + Robinson E. + Hardman C.A. + Halford J.C.G. + Jones A. Eating under observation: a systematic review and meta-analysis of the effect that heightened awareness of observation has on laboratory measured energy intake. Am. J. Clin. Nutr. 2015; 102: 324-337 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 105. + Rodriguez-Palacios A. + Harding A. + Menghini P. + Himmelman C. + Retuerto M. + Nickerson K.P. + Lam M. + Croniger C.M. + McLean M.H. + Durum S.K. + et al. The artificial sweetener Splenda promotes gut Proteobacteria, dysbiosis, and myeloperoxidase reactivity in Crohn's disease-like ileitis. Inflamm. Bowel Dis. 2018; 24: 1005-1020 View in Article + Scopus (91) + PubMed + Crossref + Google Scholar 106. + Romo-Romo A. + Aguilar-Salinas C.A. + Brito-Cordova G.X. + Gomez Diaz R.A. + Vilchis Valentin D. + Almeda-Valdes P. Effects of the non-nutritive sweeteners on glucose metabolism and appetite regulating hormones: systematic review of observational prospective studies and clinical trials. PLoS One. 2016; 11: e0161264 View in Article + Scopus (64) + PubMed + Crossref + Google Scholar 107. + Romo-Romo A. + Aguilar-Salinas C.A. + Brito-Cordova G.X. + Gomez-Diaz R.A. + Almeda-Valdes P. Sucralose decreases insulin sensitivity in healthy subjects: a randomized controlled trial. Am. J. Clin. Nutr. 2018; 108: 485-491 View in Article + Scopus (38) + PubMed + Crossref + Google Scholar 108. + Roth-Schulze A.J. + Penno M.A.S. + Ngui K.M. + Oakey H. + Bandala-Sanchez E. + Smith A.D. + Allnutt T.R. + Thomson R.L. + Vuillermin P.J. + Craig M.E. + et al. Type 1 diabetes in pregnancy is associated with distinct changes in the composition and function of the gut microbiome. Microbiome. 2021; 9: 167 View in Article + Scopus (7) + PubMed + Crossref + Google Scholar 109. + Routy B. + Le Chatelier E. + Derosa L. + Duong C.P.M. + Alou M.T. + Daillere R. + Fluckiger A. + Messaoudene M. + Rauber C. + Roberti M.P. + et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science. 2018; 359: 91-97 View in Article + Scopus (2267) + PubMed + Crossref + Google Scholar 110. + Sanchez-Tapia M. + Miller A.W. + Granados-Portillo O. + Tovar A.R. + Torres N. The development of metabolic endotoxemia is dependent on the type of sweetener and the presence of saturated fat in the diet. Gut Microbes. 2020; 12: 1801301 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 111. + Schiffman S.S. + Rother K.I. Sucralose, a synthetic organochlorine sweetener: overview of biological issues. J. Toxicol. Environ. Health B Crit. Rev. 2013; 16: 399-451 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 112. + Schleheck D. + Cook A.M. Saccharin as a sole source of carbon and energy for Sphingomonas xenophaga SKN. Arch. Microbiol. 2003; 179: 191-196 View in Article + Scopus (15) + PubMed + Crossref + Google Scholar 113. + Serrano J. + Smith K.R. + Crouch A.L. + Sharma V. + Yi F. + Vargova V. + LaMoia T.E. + Dupont L.M. + Serna V. + Tang F. + et al. High-dose saccharin supplementation does not induce gut microbiota changes or glucose intolerance in healthy humans and mice. Microbiome. 2021; 9: 11 View in Article + Scopus (11) + PubMed + Crossref + Google Scholar 114. + Shi Q. + Cai L. + Jia H. + Zhu X. + Chen L. + Deng S. Low intake of digestible carbohydrates ameliorates duodenal absorption of carbohydrates in mice with glucose metabolism disorders induced by artificial sweeteners. J. Sci. Food Agric. 2019; 99: 4952-4962 View in Article + Scopus (4) + PubMed + Crossref + Google Scholar 115. + Sims J. + Roberts A. + Daniel J.W. + Renwick A.G. The metabolic fate of sucralose in rats. Food Chem. Toxicol. 2000; 38: S115-S121 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 116. + Spencer M.D. + Hamp T.J. + Reid R.W. + Fischer L.M. + Zeisel S.H. + Fodor A.A. Association between composition of the human gastrointestinal microbiome and development of fatty liver with choline deficiency. Gastroenterology. 2011; 140: 976-986 View in Article + Scopus (414) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 117. + Suez J. + Korem T. + Zeevi D. + Zilberman-Schapira G. + Thaiss C.A. + Maza O. + Israeli D. + Zmora N. + Gilad S. + Weinberger A. + et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514: 181-186 View in Article + Scopus (1108) + PubMed + Crossref + Google Scholar 118. + Sunderhauf A. + Pagel R. + Kunstner A. + Wagner A.E. + Rupp J. + Ibrahim S.M. + Derer S. + Sina C. Saccharin supplementation inhibits bacterial growth and reduces experimental colitis in mice. Nutrients. 2020; 12: 1122 View in Article + Scopus (7) + Crossref + Google Scholar 119. + Suzek B.E. + Huang H. + McGarvey P. + Mazumder R. + Wu C.H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics. 2007; 23: 1282-1288 View in Article + Scopus (730) + PubMed + Crossref + Google Scholar 120. + Sweatman T.W. + Renwick A.G. + Burgess C.D. The pharmacokinetics of saccharin in man. Xenobiotica. 1981; 11: 531-540 View in Article + PubMed + Crossref + Google Scholar 121. + Swithers S.E. Artificial sweeteners produce the counterintuitive effect of inducing metabolic derangements. Trends Endocrinol. Metab. 2013; 24: 431-441 View in Article + Scopus (259) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 122. + Swithers S.E. + Baker C.R. + Mccurley M. + Davidson T.L. Persistent effects of high-intensity sweeteners on body weight gain in rats. Appetite. 2008; 51: 403 View in Article + Crossref + Google Scholar 123. + Sylvetsky A.C. + Bauman V. + Blau J.E. + Garraffo H.M. + Walter P.J. + Rother K.I. Plasma concentrations of sucralose in children and adults. Toxicol. Environ. Chem. 2017; 99: 535-542 View in Article + Scopus (11) + PubMed + Crossref + Google Scholar 124. + Sylvetsky A.C. + Jin Y. + Clark E.J. + Welsh J.A. + Rother K.I. + Talegawkar S.A. Consumption of low-calorie sweeteners among children and adults in the United States. J. Acad. Nutr. Diet. 2017; 117: 441-448.e2 View in Article + Scopus (132) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 125. + Sylvetsky A.C. + Walter P.J. + Garraffo H.M. + Robien K. + Rother K.I. Widespread sucralose exposure in a randomized clinical trial in healthy young adults. Am. J. Clin. Nutr. 2017; 105: 820-823 View in Article + Scopus (20) + PubMed + Crossref + Google Scholar 126. + Tang W.H.W. + Wang Z. + Levison B.S. + Koeth R.A. + Britt E.B. + Fu X. + Wu Y. + Hazen S.L. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N. Engl. J. Med. 2013; 368: 1575-1584 View in Article + Scopus (1881) + PubMed + Crossref + Google Scholar 127. + Tate D.F. + Turner-McGrievy G. + Lyons E. + Stevens J. + Erickson K. + Polzien K. + Diamond M. + Wang X. + Popkin B. Replacing caloric beverages with water or diet beverages for weight loss in adults: main results of the Choose Healthy Options Consciously Everyday (CHOICE) randomized clinical trial. Am. J. Clin. Nutr. 2012; 95: 555-563 View in Article + Scopus (255) + PubMed + Crossref + Google Scholar 128. + Thomson P. + Santibanez R. + Aguirre C. + Galgani J.E. + Garrido D. Short-term impact of sucralose consumption on the metabolic response and gut microbiome of healthy adults. Br. J. Nutr. 2019; 122: 856-862 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 129. + Tirosh A. + Calay E.S. + Tuncman G. + Claiborn K.C. + Inouye K.E. + Eguchi K. + Alcala M. + Rathaus M. + Hollander K.S. + Ron I. + et al. The short-chain fatty acid propionate increases glucagon and FABP4 production, impairing insulin action in mice and humans. Sci. Transl. Med. 2019; 11https://doi.org/10.1126/ scitranslmed.aav0120 View in Article + Scopus (104) + PubMed + Crossref + Google Scholar 130. + Toews I. + Lohner S. + Kullenberg de Gaudry D. + Sommer H. + Meerpohl J.J. Association between intake of non-sugar sweeteners and health outcomes: systematic review and meta-analyses of randomised and non-randomised controlled trials and observational studies. BMJ. 2019; 364: k4718 View in Article + Scopus (113) + PubMed + Crossref + Google Scholar 131. + Tovar A.P. + Navalta J.W. + Kruskall L.J. + Young J.C. The effect of moderate consumption of non-nutritive sweeteners on glucose tolerance and body composition in rats. Appl. Physiol. Nutr. Metab. 2017; 42: 1225-1227 View in Article + Scopus (2) + PubMed + Crossref + Google Scholar 132. + Turner A. + Veysey M. + Keely S. + Scarlett C.J. + Lucock M. + Beckett E.L. Intense sweeteners, taste receptors and the gut microbiome: A metabolic health perspective. Int. J. Environ. Res. Public Health. 2020; 17: 4094 View in Article + Scopus (7) + Crossref + Google Scholar 133. + Uebanso T. + Ohnishi A. + Kitayama R. + Yoshimoto A. + Nakahashi M. + Shimohata T. + Mawatari K. + Takahashi A. Effects of low-dose non-caloric sweetener consumption on gut microbiota in mice. Nutrients. 2017; 9: 560https://doi.org/10.3390/nu9060560 View in Article + Scopus (0) + Crossref + Google Scholar 134. + US Food and Drug Administration Additional information about high-intensity sweeteners permitted for use in food in the United States. Food Additives and Petitions. 2018; View in Article + Google Scholar 135. + Vamanu E. + Pelinescu D. + Gatea F. + Sarbu I. Altered in vitro metabolomic response of the human microbiota to sweeteners. Genes (Basel). 2019; 10: 535 View in Article + Scopus (0) + Crossref + Google Scholar 136. + von Poser Toigo E. + Huffell A.P. + Mota C.S. + Bertolini D. + Pettenuzzo L.F. + Dalmaz C. Metabolic and feeding behavior alterations provoked by prenatal exposure to aspartame. Appetite. 2015; 87: 168-174 View in Article + Scopus (29) + PubMed + Crossref + Google Scholar 137. + Vos M.B. + Kaar J.L. + Welsh J.A. + Van Horn L.V. + Feig D.I. + Anderson C.A. + Patel M.J. + Cruz Munos J. + Krebs N.F. + Xanthakos S.A. + Johnson R.K. Added sugars and cardiovascular disease risk in children: a scientific statement from the American Heart Association. Circulation. 2017; 135: e1017-e1034 View in Article + Scopus (270) + PubMed + Crossref + Google Scholar 138. + Wang Q.P. + Browman D. + Herzog H. + Neely G.G. Non-nutritive sweeteners possess a bacteriostatic effect and alter gut microbiota in mice. PLoS One. 2018; 13: e0199080 View in Article + PubMed + Google Scholar 139. + Wheeler A. + Boileau A.C. + Winkler P.C. + Compton J.C. + Prakash I. + Jiang X. + Mandarino D.A. Pharmacokinetics of rebaudioside A and stevioside after single oral doses in healthy men. Food Chem. Toxicol. 2008; 46: S54-S60 View in Article + Scopus (0) + PubMed + Crossref + Google Scholar 140. + Wood D.E. + Lu J. + Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019; 20: 257 View in Article + Scopus (881) + PubMed + Crossref + Google Scholar 141. + Wood S.G. + John B.A. + Hawkins D.R. The pharmacokinetics and metabolism of sucralose in the dog. Food Chem. Toxicol. 2000; 38: S99-S106 View in Article + PubMed + Crossref + Google Scholar 142. + Wu H. + Tremaroli V. + Schmidt C. + Lundqvist A. + Olsson L.M. + Kramer M. + Gummesson A. + Perkins R. + Bergstrom G. + Backhed F. The gut microbiota in prediabetes and diabetes: a population-based cross-sectional study. Cell Metab. 2020; 32: 379-390.e3 View in Article + Scopus (80) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 143. + Wulff J.E. + Mitchell M.W. A comparison of various normalization methods for LC/MS metabolomics data. Adv. Biosci. Biotechnol. 2018; 09: 339-351 View in Article + Crossref + Google Scholar 144. + Yu D. + Richardson N.E. + Green C.L. + Spicer A.B. + Murphy M.E. + Flores V. + Jang C. + Kasza I. + Nikodemova M. + Wakai M.H. + Tomasiewicz J.L. The adverse metabolic effects of branched-chain amino acids are mediated by isoleucine and valine. Cell metabolism. 2021; 33: 905-922 View in Article + Scopus (0) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 145. + Yu Z. + Wang Y. + Lu J. + Bond P.L. + Guo J. Nonnutritive sweeteners can promote the dissemination of antibiotic resistance through conjugative gene transfer. ISME J. 2021; 15: 2117-2130 View in Article + Scopus (41) + PubMed + Crossref + Google Scholar 146. + Zeevi D. + Korem T. + Zmora N. + Israeli D. + Rothschild D. + Weinberger A. + Ben-Yacov O. + Lador D. + Avnit-Sagi T. + Lotan-Pompan M. + et al. Personalized nutrition by prediction of glycemic responses. Cell. 2015; 163: 1079-1094 View in Article + Scopus (1202) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar 147. + Zheng L. + Cardaci S. + Jerby L. + MacKenzie E.D. + Sciacovelli M. + Johnson T.I. + Gaude E. + King A. + Leach J.D.G. + Edrada-Ebel R. + et al. Fumarate induces redox-dependent senescence by modifying glutathione metabolism. Nat. Commun. 2015; 6: 6001 View in Article + PubMed + Crossref + Google Scholar 148. + Zheng Z. + Xiao Y. + Ma L. + Lyu W. + Peng H. + Wang X. + Ren Y. + Li J. Low dose of sucralose alter gut microbiome in mice. Front. Nutr. 2022; 9: 848392 View in Article + Scopus (1) + PubMed + Crossref + Google Scholar 149. + Zmora N. + Zilberman-Schapira G. + Suez J. + Mor U. + Dori-Bachash M. + Bashiardes S. + Kotler E. + Zur M. + Regev-Lehavi D. + Brik R.B.-Z. + et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell. 2018; 174: 1388-1405.e21 View in Article + Scopus (602) + PubMed + Abstract + Full Text + Full Text PDF + Google Scholar Article Info Publication History Published: August 19, 2022 Accepted: July 18, 2022 Received in revised form: April 26, 2022 Received: January 3, 2022 Publication stage In Press, Journal Pre-proof Identification DOI: https://doi.org/10.1016/j.cell.2022.07.016 Copyright (c) 2022 Elsevier Inc. 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