Network Meta-analyses of Artificially Sweetened Beverages and Cardiometabolic Risk

Background: Sugars especially in form or sugar-sweetened beverages (SSBs) have been singled out as one of the prime culprits in the dual epidemics of obesity and diabetes. Artificially sweetened beverages (ASBs) provide a potentially important means for displacing excess calories from free sugars in the diet. There is, however, a concern that the use of ASBs may themselves contribute to an increased risk of obesity and diabetes. This concern led the 2015 Dietary Guidelines for American Committee (DGAC) to recommend that sugars in the diet not be replaced with ASBs but rather with "healthy options" such as water. Whether ASBs as a replacement strategy for SSBs have the intended benefits and whether these benefits are similar to those of the preferred replacement strategy water remains unclear.

Objective: To inform the update of the European Association of the Study (EASD) clinical practice guidelines for nutrition therapy, the investigators will conduct a series of systematic reviews and network meta-analyses to asses the effect of the substitution of ASBs for SSBs, water for SSBs, and ASBs for water on measures of adiposity and cardiometabolic risk factors.

Design: Each systematic review and meta-analysis will be conducted according to the Cochrane Handbook for Systematic Reviews of Interventions and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

Data sources: MEDLINE, EMBASE, and The Cochrane Central Register of Controlled Trials (Clinical Trials; CENTRAL) will be searched using appropriate search terms supplemented by hand searches of references of included studies. Abstracts will be included and no language restrictions will be used.

Study selection: The investigators will include randomized controlled trials (RCTs) that are >=7 days duration and assess the effect of water versus ASBs on incident overweight/obesity or cardiometabolic risk factors.

Data extraction: Two or more investigators will independently extract relevant data and assess risk of bias using the Cochrane Risk of Bias Tool. All disagreements will be resolved by consensus. Standard computations and imputations will be used to derive missing variance data.

Outcomes: There will be 9 outcome clusters. The primary outcome will be body weight. Secondary outcomes will be other markers of adiposity (BMI, body fat, waist circumference); glycemic control (glycated blood proteins [HbA1c, fasting blood glucose, postprandial blood glucose, fasting blood insulin, homeostasis model assessment of insulin resistance [HOMA-IR]); established therapeutic lipid targets (LDL-cholesterol, non-HDL-cholesterol, apolipoprotein B [apo B], HDL-cholesterol, triglycerides, total cholesterol, ); blood pressure (systolic blood pressure and diastolic blood pressure); markers of NAFLD (intrahepatocellular lipids [IHCL], alanine aminotransferase [ALT], aspartate aminotransferase [AST]); and uric acid.

Data synthesis: Risk ratios (incident overweight/obesity) and mean differences (all other outcomes) will be pooled for direct comparisons (ASBs versus water) and indirect comparisons (ASBs versus SSBs and water versus SSBs using SSBs as the common comparator). We will perform Bayesian Network-Meta Analysis (NMA). We will present the pooled estimates as posterior means and 95% credible intervals. We will also use NMA for sensitivity analysis to validate our findings from the conventional generic inverse variance random-effects models. The NMA will be conducted using Markov Chain Monte Carlo (MCMC) approach simulation using GeMTC-R package. Paired analyses will be applied for crossover trials. Heterogeneity will be assessed by the Cochran Q statistic and quantified by the I2 statistic. To explore sources of heterogeneity, the investigators will conduct sensitivity analyses, in which each study is systematically removed. If there are >=10 studies, then the investigators will also explore sources of heterogeneity by a priori subgroup analyses by health status (metabolic syndrome/diabetes, overweight, normal weight), dose (<=median intake, >median intake), baseline measurements (global and abdominal adiposity), randomization, study design (parallel, crossover), energy balance (positive, neutral, negative), follow-up (<=8 weeks, >8 weeks), risk of bias, and individual domains of risk of bias using meta-regression analyses. Meta-regression analyses will assess the significance of categorical and continuous subgroup analyses. When >=10 studies are available, publication bias will be investigated by inspection of funnel plots and formal testing using the Egger test and the Begg test. If publication bias is suspected, then the investigators will attempt to adjust for funnel plot asymmetry by imputing the missing study data using the Duval and Tweedie trim and fill method.

Evidence Assessment: The certainty of the evidence for each outcome will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

Knowledge translation plan: The results will be disseminated through interactive presentations at local, national, and international scientific meetings and publication in high impact factor journals. Target audiences will include the public health and scientific communities with interest in nutrition, diabetes, obesity and cardiovascular disease. Feedback will be incorporated and used to improve the public health message and key areas for future research will be defined. Applicant/Co-applicant decision makers will network among opinion leaders to increase awareness and participate directly as committee members in the development of future guidelines.

Significance: The proposed project will aid in knowledge translation related to the role of ASBs in the development of overweight and obesity, strengthening the evidence-base for guidelines and improving health outcomes by educating healthcare providers and patients, stimulating industry innovation, and guiding future research design.