Strategies To OPpose Sugars With Non-nutritive Sweeteners Or Water (STOP Sugars NOW) Trial

A Randomized Controlled Trial of the Effect of Replacing Sugar-sweetened Beverages With Non-nutritive Sweetened Beverages or Water on Gut Microbiome and Metabolic Outcomes: STOP Sugars NOW Trial

Health authorities recommend a reduction in added sugars from sugar-sweetened beverages (SSBs) due to risk of obesity and diabetes. As a sugar-reduction strategy, finding the ideal SSB replacement is of the utmost importance. Those who are already consuming SSBs might not easily replace it with water and therefore non-nutritive sweetened beverages (NSBs) present a sweetened alternative, though guidelines recommend water instead of NSBs as a replacement for SSBs. Recent evidence suggests that saccharine, a non-nutritive sweetener, which is not found in NSBs, might induce glucose intolerance by altering gut microbiota in humans. It is currently not known if replacing SSBs with NSBs (which contain low-calorie sweeteners other than saccharine) or water will have any effect on the human gut microbiota and any downstream diabetic risk. The investigators plan to undertake a randomized controlled cross-over trial in 75 healthy adults to assess the effect of replacing SSBs with equal amounts of NSBs or water for 4 weeks on the composition and diversity of human gut microbiota, changes in glucose tolerance and total body fat in those who regularly drink SSBs. Each participant will act as their own control receiving each of the three interventions of SSB, NSB and water for four weeks in random order, each period separated by a four-week wash-out period. All study visits will occur at the Clinical Nutrition and Risk Factor Modification Centre at St. Michael's Hospital. This study will contribute to knowledge that will inform dietary guidelines and public policy with regards to the best possible replacement for SSBs. It will also shed light on the potential mechanism of the adverse effects of NSBs and if the replacement of SSBs by NSBs or water are in fact similar with respect to their effect on gut bacteria and any downstream diabetic risk.

BACKGROUND AND SIGNIFICANCE: International health agencies and chronic disease associations have called for reductions in free/added sugars to ≤5-10% of energy to address the growing epidemics of obesity and diabetes. Attention has focused especially on the reduction of major source of free sugars, sugar sweetened beverages (SSBs), of which the excess consumption has been associated with weight gain, diabetes, and their downstream complications including hypertension and coronary heart disease (CHD). Ontario's Healthy Kids Panel, Health Canada, the Standing Senate Committee, the Heart and Stroke Foundation and Diabetes Canada have recommended policies to reduce SSBs including replacement strategies, taxation, and/or bans on advertising to children. A role for non-nutritive sweeteners (NNSs) in these policy options has been conspicuously absent. There is an emerging concern that NNSs may contribute to an increase in the diseases that they are trying to prevent. Systematic reviews and meta-analyses of prospective cohort studies have shown that NNSs are associated with increased risk of weight gain, diabetes, and CHD. Although this evidence is recognized to be at high risk of reverse causality and disagrees with the higher quality evidence from randomized controlled trials, several biological mechanisms have been proposed, among them changes in gut microbiome. One highly-influential study concluded that NNSs induce glucose intolerance through a loss of diversity in microbiome. This study, however, disagreed with a subsequent study and had several methodological weaknesses including the lack of a control group. Despite the uncertainties, these data have contributed to a negative view of NNSs in the media. There is an urgent need to address the ongoing concerns related to NNSs. Health Canada, in particular, has indicated that studies of sugar reduction strategies that use NNSs and target microbiome are an important research priority. The investigators propose to conduct a CIHR-funded randomized controlled trial that assesses the effect of a 'real world' strategy to reduce SSBs using non-nutritive sweetened beverages (NSBs) or water on gut microbiome, glucose tolerance, and cardiometabolic risk factors in overweight or obese participants. OBJECTIVES To assess the effect of replacing SSBs with NSBs or water on the first primary outcome of diversity of gut microbiome over 4-weeks in overweight or obese participants. To assess the effect of replacing SSBs with NSBs or water on the second primary outcome of glucose tolerance over 4-weeks in overweight or obese participants. To assess the effect of replacing SSBs with NSBs or water on the secondary and exploratory outcomes of body weight, blood pressure, glucose and insulin regulation, blood lipids, ectopic fat, liver fat, body adiposity, and diet quality over 4-weeks in overweight or obese participants PARTICIPANTS: Participants will be recruited from a population of healthy, adult men and non-pregnant women who are overweight or obese (BMI > 23 kg/m2 for Asian individuals and > 25 kg/m2 other individuals) who currently report drinking SSBs regularly (≥ 1 serving daily). 75 participants will be recruited for the study. Of the 75 participants, 30 of them will be asked to consent to have an MRI taken to measure their liver and muscle adiposity. DESIGN: The trial is a four-week single-centre, open-label, randomized controlled cross-over trial with three arms (SSB, NSB, water) comparing the effect of replacing SSBs with NSBs or water on the gut microbiome. Each participant will act as their own control receiving the interventions for four weeks in random order, with intervention phases separated by four-week wash-out phases. POWER CALCULATION: The study will be performed in a total of 75 participants. It is powered to show a difference between the water, NSB, and SSB arms in 60 participants in the two primary outcomes. Assuming a drop-out rate of 20 percent, we would need 75 participants in order to have to power to detect a difference. The first primary outcome is in beta diversity of the gut microbiome communities of the participants between water and NSB groups via 16S ribosomal rRNA gene sequencing. The investigators used the micropower R package to compute sample size based upon the power of 16S tag sequencing that can be analyzed using pairwise weighted UniFrac distances. UniFrac is a distance metric based upon the unique fraction of branch length in a phylogenetic tree built from two sets of taxa. Comparison of microbiome samples is performed via weighted UniFrac, which considers the relative abundance of taxa. The investigators simulated the within-group distance as 0.2, and the standard deviation (SD) of within-group distances as 0.07. To detect a weighted UniFrac distance of 0.04, which is smaller than the effect observed in a studies of Suez et al. (0.05 derived from figure 5), and considering it is a cross-over study with a within-person correlation of 0.7, and taking into account multiple arms the investigators calculated that for above 95 percent power the investigators would need 60 participants in this study. Assuming a loss of 20 percent, the investigators will recruit 75 participants. The investigators are confident about detecting an alteration in gut microbiome diversity if it exists as previous studies show that small changes in diet causes significant alteration in gut microbiome taxa over a much shorter period (5 to 7 days) in fewer individuals (10 to 25 people). The second primary outcome is glucose tolerance, as measured by incremental Area Under the Curve (iAUC) from a 2-hour 75g OGTT. With 60 participants the investigators will have 89 percent power (assuming absolute numbers for mean and SD from the investigators recent unpublished randomized trial) to detect a 20% change in mean iAUC between the water and NSB group if the direction of change is similar to Suez et al. while assuming a within-person correlation of 0.7 and taking into account the three comparisons. The 20% difference for glucose iAUC is based on the minimally important difference proposed by Health Canada to support postprandial blood glucose response reduction claims. This power calculation takes into account adjustment for multiple testing for both primary outcomes using the Benjamini-Hochberg procedure, which is a suggested method by the Food and Drug Administration in its "Multiple Endpoints in Clinical Trials Guidance for Industry" (https://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm536750.pdf). Benjamini-Hochberg procedure is a step-down method that controls for false discovery rate, while maintaining high power. The investigators will implement a truncated Benjamini-Hochberg method with parallel gatekeeping in which some portion of the unused alpha from each step is reserved for passing to the secondary outcome family if any of the primary outcome is significant. The alpha levels calculated for the primary outcomes is given in table 2. The study is also powered to show a difference between the three arms for secondary outcomes with an α=0.0125, the lowest possible starting α for secondary outcomes based upon the truncated Benjamini-Hochberg procedure. Sub-study: 1H-MRS will be performed in 30 subjects to assess intrahepatic and intramuscular fat. The investigators will have 99% power to show a difference in liver fat of 5% between the water and NSB arm for both hepatic and muscle fat assuming a between group SD of 4%, with a correlation of 0.65 and alpha of 0.05. RECRUITMENT: Using the Research Electronic Data Capture (REDCap) program, the Applied Health Research Centre (AHRC) will perform the randomization with no stratification. Following successful completion of the run-in phase and following measurements taken at the first study visit, participants will be randomized into groups, of a possible six, using a blocked (Latin squares) randomization. These groups will be sequences representing SSB, NSB and water groups. The Latin square sequences will be randomly allocated to participants with a similar number of participants allocated to each treatment sequence. The randomization schedule is also created by AHRC through REDCap. The participants will only be randomized and given their study drinks once all measures from the first study visit are collected. INTERVENTIONS: There will be three interventions: Participants will be provided with the SSB of their choice (355 ml, 140kcal, 39g sugars per can), equivalent NSB (355 ml, 0kcal, 0g sugars per can), or water (355 ml, 0kcal, 0g sugars per can or bottle of still or carbonated water) to replace the amount of SSBs they usually consume (≥1serving/day) as determined in the run-in phase. All intervention beverages will be provided to each participant. They will be instructed to replace their usual SSBs with the study beverages while freely consuming their usual background diets. The calories of the intervention groups will not be matched to allow for "real-world" substitutions using products available on the market. They will pick up one week of their beverage assignment at the first visit of each phase and then will have the remaining three weeks of beverages delivered using an online grocery delivery service. The participants will receive relevant drinks during the intervention phase based on their group assignment. They will revert to their usual SSB intake during wash-out phase during which they will not receive any beverages from the study. STATISTICAL ANALYSIS: Data will be analyzed according to an intention to treat (ITT) principle using mixed models in STATA 14 (StataCorp, Texas, USA). Sensitivity analysis will be performed on the basis of complete data availability for primary endpoints. A separate sensitivity analysis will be performed on the basis of antibiotic use during the trial. Primary outcomes. Repeated measures mixed effect models will be used to assess changes in the two primary outcomes i) beta diversity and ii) glucose iAUC between the groups. Pairwise comparisons between interventions will be performed using Tukey-Kramer adjustment or other appropriate statistics. For all primary outcomes effect modification by sex will be explored. The investigators will use the truncated Benjamini-Hochberg false discovery rate controlling method with parallel gatekeeping procedure to correct for multiple comparisons for all primary outcomes. Secondary outcomes. Repeated measures mixed effect models be used to assess changes in weight, waist circumference, fasting glucose, 2hr plasma glucose, and MATSUDA. Pairwise comparisons between interventions will be performed using Tukey-Kramer adjustment or other appropriate statistics. For all secondary outcomes effect modification by sex will be explored. The investigators will use the truncated Benjamini-Hochberg false discovery rate controlling method with parallel gatekeeping procedure to correct for multiple comparisons for all secondary outcome comparisons if at least one primary outcome reaches significance. If none of the primary outcomes reach significance, the secondary outcomes will be analyzed as exploratory variables with no adjustment for false discovery rate. Exploratory and adherence outcomes. Repeated measures mixed effect models will be used to assess changes in all exploratory outcomes without controlling for false discovery rate. Pairwise comparisons between interventions will be performed using Tukey-Kramer adjustment or other appropriate statistics. Effect modification by sex will be explored. OUTCOMES: The two primary outcomes are change in gut microbiome beta diversity, measured by 16S rRNA gene sequencing, and plasma glucose iAUC, measured by OGTT. Secondary outcomes are change in waist circumference, body weight, fasting plasma glucose, 2h plasma glucose [2h-PG], and the Matsuda whole body insulin sensitivity index [Matsuda ISIOGTT]. Exploratory outcomes (specified below as "Other Pre-specified Outcome Measures") represent a comprehensive but non-exhaustive list of potential outcomes to be assessed which will be conducted on an ad hoc basis depending on the availability of funding. These include change in ectopic fat (an early metabolic lesion) in liver (intra-hepatocellular lipid [IHCL]) and calf muscles (intra-myocellular lipid [IMCL]) by 1H-MRS; fasting plasma insulin; 75g OGTT derived indices (iAUC plasma insulin, maximum concentrations (Cmax) and time to maximum concentrations (Tmax) of plasma glucose and insulin, and mean incremental plasma glucose and insulin); homeostatic model assessment of insulin resistance (HOMA IR); the insulin secretion-sensitivity index-2 [ISSI-2]); fasting blood lipid profile; satiety, hunger, and food cravings (using the Control of Eating Questionnaire); diet quality (by analysis of the 3DDRs); and cardiometabolic risk (systolic and diastolic blood pressure, lipid profile (LDL, HDL, non-HDL cholesterol, total cholesterol), CRP, urinary sodium, liver function/injury (ALT, AST, ALP, TBIL), and kidney function/injury (albumin-to-creatinine ratio [ACR], creatinine, eGFR)), metabolomics, and proteomics. Adherence outcomes will be based on participant beverage logs, returned beverage containers, and objective biomarkers of SSBs (increased 13C/12C ratios in serum fatty acids, increased urinary fructose), water (decreased 13C/12C ratios in serum fatty acids, decreased urinary fructose), and NSBs (increased urinary acesulfame potassium, sucralose) intake.

Healthy, Overweight and Obesity, Metabolic Syndrome, PreDiabetes, Diabetes, Overweight, Obesity, Dysglycemia, Cardiovascular Diseases, Cardiovascular Risk Factor, Hypertension

Microbiome, Sugar Sweetened Beverages, Diet Beverages, Non-Nutritive Sweetened Beverages, Glucose response

The SSB intervention will consist the participants' consuming their usual serving of cans SSBs (each 355 ml, 42 grams sugar) per day. The calories of the SSB group will not be matched to allow for "real-world" substitutions using products available on the market.

The NSB intervention consists of substituting the participants' usual serving of cans SSBs with NSBs (each 355 ml, 0 grams sugar) per day. The calories of the NSB group will not be matched to allow for "real-world" substitutions using products available on the market.

The water intervention consists of substituting the participants' usual serving of cans SSBs with bottles or cans of still or sparkling water (each 355 ml bottle or can, 0 grams sugar) per day. The calories of the water group will not be matched to allow for "real-world" substitutions using products available on the market.

SSBs will be provided to each participant. Participants will be able to choose their SSB of choice from the list in the protocol. They will be instructed to drink their usual SSB intake, study drinks provided, while freely consume their usual background diets. They will revert to their usual SSB intake during wash-out phase during which they will not receive any beverage drinks from the study.

NSBs will be provided to each participant. Participants will be be given the NSB equivalent to the usual SSB chosen from the list in the protocol. They will be instructed to replace their usual SSBs with the NSBs while freely consume their usual background diets. They will revert to their usual SSB intake during wash-out phase during which they will not receive any beverage drinks from the study.

Water will be provided to each participant. They will be instructed to replace their usual SSBs with the water while freely consume their usual background diets. They will revert to their usual SSB intake during wash-out phase during which they will not receive any beverage drinks from the study.

75g OGTT derived maximum concentrations (Cmax) and time to maximum concentrations (Tmax) of plasma glucose

75g OGTT derived maximum concentrations (Cmax) and time to maximum concentrations (Tmax) of plasma insulin

Adherence markers - Objective biomarkers of SSBs (increased 13C/12C ratios in serum fatty acids and increased urinary fructose)

Adherence markers - Objective biomarkers water (decreased 13C/12C ratios in serum fatty acids and decreased urinary fructose)

Adherence markers - Objective biomarkers NSBs (increased urinary acesulfame potassium and/or sucralose)

Inclusion Criteria: Healthy, adult (age, 18-75 years) men and non-pregnant women; Overweight or obese (BMI > 23 kg/m2 for Asian individuals and > 25 kg/m2 other individuals); High waist circumference (> 94 cm in men, > 80 cm in women in Europid, Sub-Saharan African, Eastern Mediterranean, and Middle Eastern individuals; > 90 cm in men and > 80 cm in women for South Asian, Chinese, Japanese, and South and Central American individuals); Currently report drinking SSBs regularly (≥ 1 serving daily); Have a primary care physician; Nonsmoker; Free of any diseases or illnesses; Not regularly taking any medications that have a clinically relevant effect on the primary outcomes, as deemed inappropriate by investigators Exclusion Criteria: Age < 18 or > 75 years; BMI < 23 kg/m2 for Asian individuals and < 25 kg/m2 other individuals; Waist circumference < 94cm in men, < 80cm in women in Europid, Sub-Saharan African, Eastern Mediterranean, and Middle Eastern individuals; < 90cm in men and < 80 cm in women for South Asian, Chinese, Japanese, and South and Central American individuals; Not regularly drinking SSBs (≥1 serving per day); Pregnant or breast feeding females, or women planning on becoming pregnant throughout study duration; Regular medication use that have a clinically relevant effect on the primary outcomes, as deemed inappropriate by investigators Antibiotic use in the last 3 months; Complementary or alternative medicine (CAM) use as deemed inappropriate by investigators; Self-reported diabetes; Self-reported uncontrolled hypertension (or systolic blood pressure (BP) ≥ 160 mmHg or diastolic BP ≥ 100 mmHg [26]); Self-reported polycystic ovarian syndrome; Self-reported cardiovascular disease; Self-reported gastrointestinal disease; Previous bariatric surgery; Self-reported liver disease; Self-reported uncontrolled hyperthyroidism or hypothyroidism; Self-reported kidney disease; Self-reported chronic infection; Self-reported lung disease; Self-reported cancer/malignancy; Self-reported schizophrenia spectrum and other psychotic disorders, bipolar and related disorders, and dissociative disorders; Major surgery in the last 6 months; Other major illness or health-related incidence within the last 6 months; Smoker; Regular recreational drug users; Heavy alcohol use (> 3 drinks/day); Do not have a primary care physician; Participation in any trials within the last 6 months or for the duration of this study; Individuals planning on making dietary or physical activity changes throughout study duration; If participating in MRI portion of study: any condition or circumstance which would prevent the participant from having an MRI (e.g. having prostheses or metal implants, tattoos, or claustrophobia)

Dietary Guidelines Advisory Committee, 2015-2020 Dietary Guidelines for Americans - health.gov. 2015.

InterAct Consortium; Romaguera D, Norat T, Wark PA, Vergnaud AC, Schulze MB, van Woudenbergh GJ, Drogan D, Amiano P, Molina-Montes E, Sanchez MJ, Balkau B, Barricarte A, Beulens JW, Clavel-Chapelon F, Crispim SP, Fagherazzi G, Franks PW, Grote VA, Huybrechts I, Kaaks R, Key TJ, Khaw KT, Nilsson P, Overvad K, Palli D, Panico S, Quiros JR, Rolandsson O, Sacerdote C, Sieri S, Slimani N, Spijkerman AM, Tjonneland A, Tormo MJ, Tumino R, van den Berg SW, Wermeling PR, Zamara-Ros R, Feskens EJ, Langenberg C, Sharp SJ, Forouhi NG, Riboli E, Wareham NJ. Consumption of sweet beverages and type 2 diabetes incidence in European adults: results from EPIC-InterAct. Diabetologia. 2013 Jul;56(7):1520-30. doi: 10.1007/s00125-013-2899-8. Epub 2013 Apr 26.

Greenwood DC, Threapleton DE, Evans CE, Cleghorn CL, Nykjaer C, Woodhead C, Burley VJ. Association between sugar-sweetened and artificially sweetened soft drinks and type 2 diabetes: systematic review and dose-response meta-analysis of prospective studies. Br J Nutr. 2014 Sep 14;112(5):725-34. doi: 10.1017/S0007114514001329. Epub 2014 Jun 16.

McGuire S. Scientific Report of the 2015 Dietary Guidelines Advisory Committee. Washington, DC: US Departments of Agriculture and Health and Human Services, 2015. Adv Nutr. 2016 Jan 15;7(1):202-4. doi: 10.3945/an.115.011684. Print 2016 Jan. No abstract available.

Suez J, Korem T, Zeevi D, Zilberman-Schapira G, Thaiss CA, Maza O, Israeli D, Zmora N, Gilad S, Weinberger A, Kuperman Y, Harmelin A, Kolodkin-Gal I, Shapiro H, Halpern Z, Segal E, Elinav E. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014 Oct 9;514(7521):181-6. doi: 10.1038/nature13793. Epub 2014 Sep 17.

Hartstra AV, Bouter KE, Backhed F, Nieuwdorp M. Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care. 2015 Jan;38(1):159-65. doi: 10.2337/dc14-0769.

David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014 Jan 23;505(7484):559-63. doi: 10.1038/nature12820. Epub 2013 Dec 11.

Korem T, Zeevi D, Suez J, Weinberger A, Avnit-Sagi T, Pompan-Lotan M, Matot E, Jona G, Harmelin A, Cohen N, Sirota-Madi A, Thaiss CA, Pevsner-Fischer M, Sorek R, Xavier R, Elinav E, Segal E. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science. 2015 Sep 4;349(6252):1101-1106. doi: 10.1126/science.aac4812. Epub 2015 Jul 30.

Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.

Noble EE, Hsu TM, Jones RB, Fodor AA, Goran MI, Kanoski SE. Early-Life Sugar Consumption Affects the Rat Microbiome Independently of Obesity. J Nutr. 2017 Jan;147(1):20-28. doi: 10.3945/jn.116.238816. Epub 2016 Nov 30.

Fedorak, R., et al., 546 High Sugar Diets Promote an Inflammatory Microbiota and Reduce Gene Expression Related to Intestinal Barrier Function. Gastroenterology, 2016. 150(4): p. S114-S115.

Kelly BJ, Gross R, Bittinger K, Sherrill-Mix S, Lewis JD, Collman RG, Bushman FD, Li H. Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics. 2015 Aug 1;31(15):2461-8. doi: 10.1093/bioinformatics/btv183. Epub 2015 Mar 29.

Mouzaki M, Comelli EM, Arendt BM, Bonengel J, Fung SK, Fischer SE, McGilvray ID, Allard JP. Intestinal microbiota in patients with nonalcoholic fatty liver disease. Hepatology. 2013 Jul;58(1):120-7. doi: 10.1002/hep.26319. Epub 2013 May 14.

Aagaard K, Petrosino J, Keitel W, Watson M, Katancik J, Garcia N, Patel S, Cutting M, Madden T, Hamilton H, Harris E, Gevers D, Simone G, McInnes P, Versalovic J. The Human Microbiome Project strategy for comprehensive sampling of the human microbiome and why it matters. FASEB J. 2013 Mar;27(3):1012-22. doi: 10.1096/fj.12-220806. Epub 2012 Nov 19.

Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012 Aug;6(8):1621-4. doi: 10.1038/ismej.2012.8. Epub 2012 Mar 8.

Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010 May;7(5):335-6. doi: 10.1038/nmeth.f.303. Epub 2010 Apr 11. No abstract available.

Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013 Sep;31(9):814-21. doi: 10.1038/nbt.2676. Epub 2013 Aug 25.

Kadish AH, Hall DA. A new method for the continuous monitoring of blood glucose by measurement of dissolved oxygen. Clin Chem. 1965 Sep;11(9):869-75. No abstract available.

Livesey JH, Hodgkinson SC, Roud HR, Donald RA. Effect of time, temperature and freezing on the stability of immunoreactive LH, FSH, TSH, growth hormone, prolactin and insulin in plasma. Clin Biochem. 1980 Aug;13(4):151-5. doi: 10.1016/s0009-9120(80)91040-1.

Canadian Diabetes, A. Waist Circumference. Available from: https://www.diabetes.ca/diabetes-and-you/healthy-living-resources/weight-management/waist-circumference.

Wolever TM, Jenkins DJ, Jenkins AL, Josse RG. The glycemic index: methodology and clinical implications. Am J Clin Nutr. 1991 Nov;54(5):846-54. doi: 10.1093/ajcn/54.5.846.

Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999 Sep;22(9):1462-70. doi: 10.2337/diacare.22.9.1462.

Phillips DI, Clark PM, Hales CN, Osmond C. Understanding oral glucose tolerance: comparison of glucose or insulin measurements during the oral glucose tolerance test with specific measurements of insulin resistance and insulin secretion. Diabet Med. 1994 Apr;11(3):286-92. doi: 10.1111/j.1464-5491.1994.tb00273.x.

Brisbois TD, Marsden SL, Anderson GH, Sievenpiper JL. Estimated intakes and sources of total and added sugars in the Canadian diet. Nutrients. 2014 May 8;6(5):1899-912. doi: 10.3390/nu6051899.

Singh GM, Micha R, Khatibzadeh S, Lim S, Ezzati M, Mozaffarian D; Global Burden of Diseases Nutrition and Chronic Diseases Expert Group (NutriCoDE). Estimated Global, Regional, and National Disease Burdens Related to Sugar-Sweetened Beverage Consumption in 2010. Circulation. 2015 Aug 25;132(8):639-66. doi: 10.1161/CIRCULATIONAHA.114.010636. Epub 2015 Jun 29.

Bleich SN, Wang YC, Wang Y, Gortmaker SL. Increasing consumption of sugar-sweetened beverages among US adults: 1988-1994 to 1999-2004. Am J Clin Nutr. 2009 Jan;89(1):372-81. doi: 10.3945/ajcn.2008.26883. Epub 2008 Dec 3.

Fernandes J, Su W, Rahat-Rozenbloom S, Wolever TM, Comelli EM. Adiposity, gut microbiota and faecal short chain fatty acids are linked in adult humans. Nutr Diabetes. 2014 Jun 30;4(6):e121. doi: 10.1038/nutd.2014.23.

Fernandes J, Wang A, Su W, Rozenbloom SR, Taibi A, Comelli EM, Wolever TM. Age, dietary fiber, breath methane, and fecal short chain fatty acids are interrelated in Archaea-positive humans. J Nutr. 2013 Aug;143(8):1269-75. doi: 10.3945/jn.112.170894. Epub 2013 Jun 5.

Khan TA, Sievenpiper JL. Controversies about sugars: results from systematic reviews and meta-analyses on obesity, cardiometabolic disease and diabetes. Eur J Nutr. 2016 Nov;55(Suppl 2):25-43. doi: 10.1007/s00394-016-1345-3. Epub 2016 Nov 30.

Wolf SM, Branum R, Koenig BA, Petersen GM, Berry SA, Beskow LM, Daly MB, Fernandez CV, Green RC, LeRoy BS, Lindor NM, O'Rourke PP, Breitkopf CR, Rothstein MA, Van Ness B, Wilfond BS. Returning a Research Participant's Genomic Results to Relatives: Analysis and Recommendations. J Law Med Ethics. 2015 Fall;43(3):440-63. doi: 10.1111/jlme.12288.

Malik VS, Pan A, Willett WC, Hu FB. Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr. 2013 Oct;98(4):1084-102. doi: 10.3945/ajcn.113.058362. Epub 2013 Aug 21.

Jayalath VH, de Souza RJ, Ha V, Mirrahimi A, Blanco-Mejia S, Di Buono M, Jenkins AL, Leiter LA, Wolever TM, Beyene J, Kendall CW, Jenkins DJ, Sievenpiper JL. Sugar-sweetened beverage consumption and incident hypertension: a systematic review and meta-analysis of prospective cohorts. Am J Clin Nutr. 2015 Oct;102(4):914-21. doi: 10.3945/ajcn.115.107243. Epub 2015 Aug 12.

Xi B, Huang Y, Reilly KH, Li S, Zheng R, Barrio-Lopez MT, Martinez-Gonzalez MA, Zhou D. Sugar-sweetened beverages and risk of hypertension and CVD: a dose-response meta-analysis. Br J Nutr. 2015 Mar 14;113(5):709-17. doi: 10.1017/S0007114514004383. Epub 2015 Mar 4.

Stampfer MJ, Hu FB, Manson JE, Rimm EB, Willett WC. Primary prevention of coronary heart disease in women through diet and lifestyle. N Engl J Med. 2000 Jul 6;343(1):16-22. doi: 10.1056/NEJM200007063430103.

Azad MB, Abou-Setta AM, Chauhan BF, Rabbani R, Lys J, Copstein L, Mann A, Jeyaraman MM, Reid AE, Fiander M, MacKay DS, McGavock J, Wicklow B, Zarychanski R. Nonnutritive sweeteners and cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials and prospective cohort studies. CMAJ. 2017 Jul 17;189(28):E929-E939. doi: 10.1503/cmaj.161390.

Sievenpiper JL, Khan TA, Ha V, Viguiliouk E, Auyeung R. The importance of study design in the assessment of nonnutritive sweeteners and cardiometabolic health. CMAJ. 2017 Nov 20;189(46):E1424-E1425. doi: 10.1503/cmaj.733381. No abstract available.

Frankenfeld CL, Sikaroodi M, Lamb E, Shoemaker S, Gillevet PM. 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 Oct;25(10):736-42.e4. doi: 10.1016/j.annepidem.2015.06.083. Epub 2015 Jul 17.

Schulz KF, Altman DG, Moher D; CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. Int J Surg. 2011;9(8):672-7. doi: 10.1016/j.ijsu.2011.09.004. Epub 2011 Oct 13. No abstract available.

Leung AA, Daskalopoulou SS, Dasgupta K, McBrien K, Butalia S, Zarnke KB, Nerenberg K, Harris KC, Nakhla M, Cloutier L, Gelfer M, Lamarre-Cliche M, Milot A, Bolli P, Tremblay G, McLean D, Tran KC, Tobe SW, Ruzicka M, Burns KD, Vallee M, Prasad GVR, Gryn SE, Feldman RD, Selby P, Pipe A, Schiffrin EL, McFarlane PA, Oh P, Hegele RA, Khara M, Wilson TW, Penner SB, Burgess E, Sivapalan P, Herman RJ, Bacon SL, Rabkin SW, Gilbert RE, Campbell TS, Grover S, Honos G, Lindsay P, Hill MD, Coutts SB, Gubitz G, Campbell NRC, Moe GW, Howlett JG, Boulanger JM, Prebtani A, Kline G, Leiter LA, Jones C, Cote AM, Woo V, Kaczorowski J, Trudeau L, Tsuyuki RT, Hiremath S, Drouin D, Lavoie KL, Hamet P, Gregoire JC, Lewanczuk R, Dresser GK, Sharma M, Reid D, Lear SA, Moullec G, Gupta M, Magee LA, Logan AG, Dionne J, Fournier A, Benoit G, Feber J, Poirier L, Padwal RS, Rabi DM; Hypertension Canada. Hypertension Canada's 2017 Guidelines for Diagnosis, Risk Assessment, Prevention, and Treatment of Hypertension in Adults. Can J Cardiol. 2017 May;33(5):557-576. doi: 10.1016/j.cjca.2017.03.005. Epub 2017 Mar 10. Erratum In: Can J Cardiol. 2017 Dec;33(12 ):1733-1734.

Dalton M, Finlayson G, Hill A, Blundell J. Preliminary validation and principal components analysis of the Control of Eating Questionnaire (CoEQ) for the experience of food craving. Eur J Clin Nutr. 2015 Dec;69(12):1313-7. doi: 10.1038/ejcn.2015.57. Epub 2015 Apr 8.

Blom DJ, Hala T, Bolognese M, Lillestol MJ, Toth PD, Burgess L, Ceska R, Roth E, Koren MJ, Ballantyne CM, Monsalvo ML, Tsirtsonis K, Kim JB, Scott R, Wasserman SM, Stein EA; DESCARTES Investigators. A 52-week placebo-controlled trial of evolocumab in hyperlipidemia. N Engl J Med. 2014 May 8;370(19):1809-19. doi: 10.1056/NEJMoa1316222. Epub 2014 Mar 29.

Logue C, Dowey LRC, Strain JJ, Verhagen H, McClean S, Gallagher AM. Application of Liquid Chromatography-Tandem Mass Spectrometry To Determine Urinary Concentrations of Five Commonly Used Low-Calorie Sweeteners: A Novel Biomarker Approach for Assessing Recent Intakes? J Agric Food Chem. 2017 Jun 7;65(22):4516-4525. doi: 10.1021/acs.jafc.7b00404. Epub 2017 May 24.

Li S, Zhu Y, Chavarro JE, Bao W, Tobias DK, Ley SH, Forman JP, Liu A, Mills J, Bowers K, Strom M, Hansen S, Hu FB, Zhang C. Healthful Dietary Patterns and the Risk of Hypertension Among Women With a History of Gestational Diabetes Mellitus: A Prospective Cohort Study. Hypertension. 2016 Jun;67(6):1157-65. doi: 10.1161/HYPERTENSIONAHA.115.06747. Epub 2016 Apr 18.

Nitsche MP, Carreno M. Is honey an effective treatment for acute cough in children? Medwave. 2016 May 30;16 Suppl 2:e6454. doi: 10.5867/medwave.2016.6454. English, Spanish.

Dias AG, Rousseau D, Duizer L, Cockburn M, Chiu W, Nielsen D, El-Sohemy A. Genetic variation in putative salt taste receptors and salt taste perception in humans. Chem Senses. 2013 Feb;38(2):137-45. doi: 10.1093/chemse/bjs090. Epub 2012 Nov 1.

Eny KM, Wolever TM, Fontaine-Bisson B, El-Sohemy A. Genetic variant in the glucose transporter type 2 is associated with higher intakes of sugars in two distinct populations. Physiol Genomics. 2008 May 13;33(3):355-60. doi: 10.1152/physiolgenomics.00148.2007. Epub 2008 Mar 18.

Wolf SM, Lawrenz FP, Nelson CA, Kahn JP, Cho MK, Clayton EW, Fletcher JG, Georgieff MK, Hammerschmidt D, Hudson K, Illes J, Kapur V, Keane MA, Koenig BA, Leroy BS, McFarland EG, Paradise J, Parker LS, Terry SF, Van Ness B, Wilfond BS. Managing incidental findings in human subjects research: analysis and recommendations. J Law Med Ethics. 2008 Summer;36(2):219-48, 211. doi: 10.1111/j.1748-720X.2008.00266.x.

Food and Drug Administration, Multiple Endpoints in Clinical Trials: Guidance for Industry [Draft Guidance]. 2017.

Benjamini, Y. and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency. Annals of statistics, 2001: p. 1165-1188.

Benjamini, Y. and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 1995: p. 289-300.

Noto, H., et al., Long-term Low-carbohydrate Diets and Type 2 Diabetes Risk: A Systematic Review and Meta-analysis of Observational Studies. Journal of General and Family Medicine, 2016. 17(1): p. 60-70.

Theytaz F, de Giorgi S, Hodson L, Stefanoni N, Rey V, Schneiter P, Giusti V, Tappy L. Metabolic fate of fructose ingested with and without glucose in a mixed meal. Nutrients. 2014 Jul 15;6(7):2632-49. doi: 10.3390/nu6072632.

Health Canada, Draft Guidance Document on Food Health Claims Related to the Reduction in Post-Prandial Glycaemic Response F.D. Bureau of Nutritional Sciences, Health Products and Food Branch Editor. 2013. p. 12.

Bergwall S, Johansson A, Sonestedt E, Acosta S. High versus low-added sugar consumption for the primary prevention of cardiovascular disease. Cochrane Database Syst Rev. 2022 Jan 5;1(1):CD013320. doi: 10.1002/14651858.CD013320.pub2.

https://s3.ca-central-1.amazonaws.com/ehq-production-canada/documents/attachments/9bced5c3821050c708407be04b299ac6ad286e47/000/006/633/original/Restricting_Marketing_to_Children.pdf

https://well.blogs.nytimes.com/2014/09/17/artificial-sweeteners-may-disrupt-bodys-blood-sugar-controls/

Chang K, Artificial Sweeteners May Disrupt Body's Blood Sugar Controls. The New York Times, September 17, 2014.

https://blogs.scientificamerican.com/guest-blog/artificial-sweeteners-may-have-despicable-impacts-on-gut-microbes/

Faherty S, Artificial Sweeteners May Have Despicable Impacts on Gut Microbes. Scientific American, Nov 26, 2014.

http://news.nationalgeographic.com/news/2014/09/140917-sweeteners-artificial-blood-sugar-diabetes-health-ngfood/

Vergano D, Study: Artificial Sweeteners May Trigger Blood Sugar Risks. National Geographic, September 17, 2014.

https://www.theglobeandmail.com/news/national/sugar-substitutes-linked-to-weight-gain-and-health-problems-study-says/article35704562/

Leung W, Sugar substitutes associated with weight gain and health problems, study says. Globe and Mail, July 17, 2017.

https://www.thestar.com/life/2017/07/17/more-research-needed-into-the-effects-sugar-substitutes-have-on-health.html

Brait E, More research needed into the effects sugar substitutes have on health. The Toronto Star, July 17, 2017.

FDA, Multiple Endpoints in Clinical Trials. Guidance for Industry (draft). January 2017. Food and Drug Administration Report.