Clinical trial NCT03543644: Strategies To OPpose Sugars With Non-nutritive Sweeteners Or Water (STOP Sugars NOW) Trial
Official title: 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
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Healthy
Overweight and Obesity
Metabolic Syndrome
PreDiabetes
Diabetes
Overweight
Obesity
Dysglycemia
Cardiovascular Diseases
Cardiovascular Risk Factor
Hypertension
Keywords: Microbiome
Sugar Sweetened Beverages
Diet Beverages
Non-Nutritive Sweetened Beverages
Glucose response
Lead sponsor: University of Toronto
Sponsor class: OTHER
Collaborators: name: Canadian Institutes of Health Research (CIHR)
First posted: 2018-06-01
Enrollment: 81
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Primary purpose: PREVENTION

Brief summary: 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.

Detailed description: 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

1. 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.
2. 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.
3. 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.

Eligibility criteria: 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)

Primary outcomes:
measure: Gut microbiome composition measured by 16S rRNA gene sequencing
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived plasma glucose iAUC
timeFrame: Week 0 and week 4 of each intervention
Secondary outcomes:
measure: Change in waist circumference
timeFrame: Week 0 and week 4 of each intervention
measure: Change in body weight
timeFrame: Week 0 and week 4 of each intervention
measure: Change in fasting plasma glucose
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived 2-hour plasma glucose [2h-PG]
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived Matsuda whole body insulin sensitivity index [Matsuda ISI OGTT]
timeFrame: Week 0 and week 4 of each intervention
Other outcomes:
measure: Ectopic fat in liver (intra-hepatocellular lipid [IHCL]) by 1H-MRS (sub-study, n=30)
timeFrame: Week 0 and week 4 of each intervention
measure: Ectopic fat in calf muscles (intra-myocellular lipid [IMCL]) by 1H-MRS (sub-study, n=30)
timeFrame: Week 0 and week 4 of each intervention
measure: Fasting plasma insulin
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived iAUC plasma insulin
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived maximum concentrations (Cmax) and time to maximum concentrations (Tmax) of plasma glucose
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived maximum concentrations (Cmax) and time to maximum concentrations (Tmax) of plasma insulin
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived mean incremental plasma glucose
timeFrame: Week 0 and week 4 of each intervention
measure: 75g OGTT derived mean incremental plasma insulin
timeFrame: Week 0 and week 4 of each intervention
measure: Homeostatic model assessment of insulin resistance (HOMA IR)
timeFrame: Week 0 and week 4 of each intervention
measure: Insulin secretion-sensitivity index-2 (ISSI-2)
timeFrame: Week 0 and week 4 of each intervention
measure: Satiety, hunger, and food cravings (using the Control of Eating Questionnaire)
timeFrame: Week 0 and week 4 of each intervention
measure: Diet quality by Alternative Healthy Eating Index (AHEI) (using a weighed three-day diet record)
timeFrame: Week 0 and week 4 of each intervention
measure: Adherence markers - Objective biomarkers of SSBs (increased 13C/12C ratios in serum fatty acids and increased urinary fructose)
timeFrame: Week 0 and week 4 of each intervention
measure: Adherence markers - Objective biomarkers water (decreased 13C/12C ratios in serum fatty acids and decreased urinary fructose)
timeFrame: Week 0 and week 4 of each intervention
measure: Adherence markers - Objective biomarkers NSBs (increased urinary acesulfame potassium and/or sucralose)
timeFrame: Week 0 and week 4 of each intervention
measure: Adherence markers - Beverage logs
timeFrame: Week 0 and week 4 of each intervention
measure: Adherence markers - Returned unused bottles
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - change in systolic blood pressure
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - change in diastolic blood pressure
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Lipid profile - LDL Cholesterol
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Lipid profile - HDL Cholesterol
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Lipid profile - non-HDL Cholesterol
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Lipid profile - Total Cholesterol
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Lipid profile - Triglycerides
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - C-Reactive Protein (CRP)
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - urinary sodium
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Liver function/injury by ALT
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Liver function/injury by AST
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Liver function/injury by ALP
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Liver function/injury by TBIL
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Kidney function/injury by creatinine
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Kidney function/injury by eGFR
timeFrame: Week 0 and week 4 of each intervention
measure: Cardiometabolic risk - Kidney function/injury by urinary ACR
timeFrame: Week 0 and week 4 of each intervention
measure: Urinary and blood metabolomic panel
timeFrame: Week 0 and week 4 of each intervention
measure: Urinary and blood proteomic panel
timeFrame: Week 0 and week 4 of each intervention
Locations:
city: Toronto
country: Canada
facility: St. Michael's Hospital
state: Ontario
Study officials:
affiliation: University of Toronto
name: John L Sievenpiper, MD,PhD,FRCPC
role: PRINCIPAL_INVESTIGATOR
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