Meta-analysis of Low-calorie Sweetened Beverages and Cardiometabolic Outcomes

Overview

We propose to conduct a systematic literature review and meta-analysis to assess the association of low-calorie sweetened beverages (LCSBs) on cardiometabolic outcomes in prospective cohort studies. We will be using methodological approaches (change in LCSBs intake, and/or substitution analysis) that attempt to overcome the issue of reverse causality associated with studies of LCSBs and cardiometabolic disease. Ten cardiometabolic outcomes will be assessed: 1. Global adiposity - body weight 2. Global adiposity - BMI 3. Global adiposity - body fat 4. Abdominal adiposity - waist circumference 5. Overweight/obesity incidence 6. Metabolic syndrome incidence 7. Type 2 diabetes incidence 8. Cardiovascular disease incidence 9. Cardiovascular disease mortality 10. Total mortality

Background: Low-calorie sweetened beverages (LCSBs) may provide a potentially important means for displacing excess calories from free sugars in the diet. However, prospective cohort studies suggest that the use of LCSBs may contribute to an increased risk of obesity and diabetes. These findings are likely due to methodological limitations of study design and analysis that do not account for reverse causality, where higher risk of cardiometabolic outcomes may lead to people to switch to LCSBs. There is a need for a systematic review and meta-analysis (SRMA) of prospective cohort studies to overcome these methodological limitations. Objective: We will conduct a SRMA of prospective cohort studies in human subjects that have assessed cardiometabolic outcomes using two analytical strategies: i) Assessment of change in intake of LCSBs with change in outcome (change analysis) ii) Assessment of substitution of LCSBs for sugar-sweetened beverages (SSBs) or water. Design: We will conduct a SRMA according to the Cochrane Handbook for Systematic Reviews of Interventions and report the findings according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines. Data sources: MEDLINE, EMBASE, and the Cochrane Library databases will be searched using appropriate search terms, supplemented by hand searches of references of included studies. No restriction will be placed on language. Study selection: Prospective cohort studies reporting (a) change analysis (change in intake of LCSBs with the change in outcome), and/or (b) substitution analysis (substitution of SSBs with LCSBs or water) with more than 1-year of follow-up will be used. Cohort studies that have a follow-up duration \<1 year, do not report assessment of exposure, or do not provide viable outcome data by level of exposure will be excluded. Data extraction Two or more investigators will independently extract relevant data and assess risk of bias using the Newcastle-Ottawa Scale (NOS) for prospective cohorts. All disagreements will be resolved by consensus. Risk ratios (RRs), odds ratios (ORs) and hazard ratios (HRs) for clinical outcomes in the prospective cohort studies will be extracted or derived from clinical event data across exposure categories. Outcomes: Ten cardiometabolic health outcomes will be assessed: 1. Global adiposity - body weight 2. Global adiposity - BMI 3. Global adiposity - body fat 4. Abdominal adiposity - waist circumference 5. Overweight/obesity incidence 6. Metabolic syndrome incidence 7. Type 2 diabetes incidence 8. Cardiovascular disease incidence 9. Cardiovascular disease mortality 10. Total mortality Data synthesis: Natural log-transformed RRs or HRs of clinical outcomes, comparing extreme quantiles (the highest exposure versus the lowest exposure or reference group), will be pooled separately using the generic inverse variance method with random effects models and expressed as RRs with 95% confidence intervals (CIs). Heterogeneity will be tested by Cochran's Q statistic and quantified by the I2 statistic. To explore sources of heterogeneity, we will conduct sensitivity analyses, in which each study is systematically removed. If ≥10 cohort comparisons are available, then we will perform an a-priori subgroup analyses by meta-regression for follow-up (\<10 years vs. ≥10 years), sex (males vs. females, males vs. mixed, females vs. mixed), study quality (NOS \<6 vs. ≥6) and funding source. Significant unexplained heterogeneity will be investigated by additional post hoc subgroup analyses and influence analysis. A study will be considered influential if it changes the direction or significance of the pooled estimates or the evidence of heterogeneity. Dose response estimates will be pooled using one-stage linear mixed model. When ≥10 studies are available, publication bias will be investigated by inspection of funnel plots and formal testing using the Egger and Begg tests. If publication bias is suspected, we 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 overall certainty of evidence for each outcome will be assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE). 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 to the role of the LCSBs role as a replacement strategy for SSBs, strengthening the evidence-base for guidelines and improving health outcomes by educating healthcare providers and patients, stimulating industry innovation, and guiding future research design.