Meta-analyses of Pasta as Part of Low Glycemic Index Diets and Adiposity

Background: Overweight and obesity is a key modifiable risk factor for the prevention and management of type 2 diabetes, dyslipidemia, hypertension, CVD and certain cancers. Low-GI dietary patterns have been shown to improve body weight, among other risk factors including glycemic control and blood lipids, and have been associated with risk reduction of type 2 diabetes and CVD. Current clinical practice guidelines suggest the replacement of high GI foods with low GI foods for improvement of glycemic control and cardiovascular risk factors. Pasta is an important example of a food which can lower the GI of the diet, a property that has been exploited extensively in studies of low GI dietary patterns. However, it is unclear whether pasta as part of low-GI dietary patterns will improve measures of global adiposity, including body weight in addition to other markers of adiposity.

Need for a review: The lack of high quality knowledge syntheses to support evidence-based dietary guidance on pasta represents an urgent call for stronger evidence. A systematic review and meta-analysis of randomized controlled trials remains the "Gold Standard" of evidence to support clinical practice guidelines and public health policy. By pooling the totality of the available evidence, a systematic reviews and meta-analyses of randomized controlled trials would provide the best estimate of the effect of pasta on measures of global adiposity relevant to the prevention and management of overweight and obesity.

Objectives: To provide evidence-based guidance for public health policy, health claims, and nutrition guidelines relating to pasta as part of low GI dietary pattern, the investigators will conduct a systematic review and meta-analysis of randomized controlled trials to assess the effect of past as part of a low GI diet on markers of global adiposity, including body weight in addition to others as well as regional markers of adiposity which have demonstrated clinical relevance.

Design: The planning and conduct of the proposed meta-analyses will follow the Cochrane handbook for systematic reviews of interventions. The reporting will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines.

Data sources: MEDLINE, EMBASE and The Cochrane Central Register of Controlled Trials will be searched using appropriate search terms.

Study selection: Randomized controlled trials that investigate the effect of pasta as part of a low GI dietary pattern on body weight and other markers of adiposity in humans will be included. Trials that are less than 3 weeks diet duration, do not have a control group, include pregnant women, breast feeding women or children, or do not report data on markers of body weight will be excluded.

Data extraction: Two independent investigators will extract information about study design, sample size, subject characteristics, GI/GL diet, follow-up, and the composition of the background diets. Mean ± SEM values will be extracted for all outcomes. Standard computations and imputations will be used to derive missing data. Risk of bias will be assessed using the Cochrane Collaboration risk of bias tool.

Outcome: The primary outcome will be mean differences in body weight as a global marker of adiposity which has demonstrated clinical relevance. Secondary outcomes will be mean differences in other markers of global adiposity with clinical relevance including BMI and percentage body fat, as well as regional markers of adiposity with clinical relevance including waist circumference, waist-to-hip ratio, sagittal abdominal diameter, and visceral adipose tissue [VAT] by imaging modalities.

Data synthesis: Pooled analysis will be conducted using the Generic Inverse Variance method. Separate analyses will be conducted for children and adults. Analyses will be further stratified by metabolic phenotype (normal weight, overweight/obese, metabolic syndrome criteria, diabetes, etc.). Random-effects models will be used even in the absence of statistically significant between-study heterogeneity, as they yield more conservative summary effect estimates in the presence of residual heterogeneity. Fixed-effects models will only be used where there is <5 included studies. Paired analyses will be applied to all crossover trials. Heterogeneity will be tested by the Cochran Q statistic and quantified by the I2 statistic. Sources of heterogeneity will be explored by sensitivity and subgroup analyses. If there are ≥ 10 trial comparisons, then a priori subgroup analyses will also be conducted by dose (<100g/day, ≥100g/day), comparator, follow-up (<6-months, ≥6-months), baseline body weight (BMI ≤25 >25kg/m2), design (parallel, crossover), energy balance (negative, neutral, positive), GI (absolute level [≤55, >55], within-treatment change, between-treatment change), fat intake (absolute level [<30%, ≥30% energy], within-treatment change, between-treatment change), carbohydrate intake (absolute level [<50%, ≥50% energy], within-treatment change, between-treatment change), protein intake (absolute level [<20%, ≥20% energy], within-treatment change, between-treatment change), dietary fibre intake (absolute level [<25g/day, ≥25g/day], within-treatment change, between-treatment change), and risk of bias. Meta-regression will assess the significance of categorical and continuous subgroup analyses. Non-linear relationships will be assessed by spline curve modeling (the MKSPLINE procedure) to assess whether there is a threshold for benefit. If there are ≥10 trial comparisons, then publication bias will be assessed by inspection of funnel plots and the Egger and Begg tests. In the presence of significant publication bias, an adjustment for funnel plot asymmetry will be attempted by using the Duval and Tweedie trim-and-fill method.

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

Knowledge translation plan: Results will be disseminated through traditional means such as interactive presentations at local, national, and international scientific meetings and publication in high impact factor journals. Innovative means such as webcasts with e-mail feedback mechanisms will also be used. Knowledge Users will act as knowledge brokers networking among opinion leaders and different adopter groups to increase awareness at each stage. Four Knowledge Users will also participate directly as members of nutrition guidelines committees. Target adopters will include the clinical practice, public health, industry, research communities, and patient groups. Feedback will be incorporated and used to guide analyses and improve key messages at each stage.

Significance: The proposed project will aid in knowledge translation related to the role of pasta as part of a healthy low-GI-dietary pattern on health outcomes strengthening the evidence-base for guidelines and improving health outcomes, by educating healthcare providers and patients, stimulating industry innovation, and guiding future research design.