Meta Analysis of the Effect of a Low Glycemic Index Diet and Glycemic Load on Body Weight

A low glycemic index (GI) diet has been associated with improved glycemic control in type 2 diabetes patients and a reduced risk of cardiovascular disease (CVD). Low glycemic load (GL) diets have been associated in cohort studies with a reduction in both diabetes incidence and CVD events, especially in overweight individuals, and have been recommended by the Canadian, American and European diabetes associations. Life style modification trials have shown that reducing body weight in overweight or obese individuals improves obesity-related risk factors. The process of a systematic review combines the results from many studies in order to arrive at a pooled weighted average of the true effect. The investigators propose to conduct a systematic review and meta-analysis of the highest quality evidence from randomized controlled trials to assess the effect of low GI/GL diets on body weight change. The results of this synthesis will inform clinical practice guidelines and lead to better health outcomes through informing healthcare providers and patients, stimulating industry innovation, and guiding future research.

Background: Obesity and overweight have been identified as a risk factor for developing type 2 diabetes, dyslipidemia, hypertension, CVD and certain cancers. Nutrition therapy is the first line treatment for prevention of these health conditions and as a key component during their initial and long term management. Diets based on low GI foods and low GL have been associated with risk reduction of type 2 diabetes and CVD. Current clinical practice guidelines suggest the replacement of high GI foods for low GI foods based on the improvement on glycemic control and CVD risk factors. However, there is no enough evidence to support the association between low GI/GL diets on body weight reduction.

Need for a review: The lack of high quality data in this area to support clinical practice guidelines represents an urgent call for stronger evidence. The use of meta-analyses of controlled feeding trials remains the "Goal Standard" of evidence for nutrition guidelines development.

Objectives: To provide evidence-based guidance for public health policy, health claims, and nutrition guidelines relating to low GI/GL diets, we will conduct a systematic review and meta-analysis of controlled feeding trials to assess the effect of a low GI/GL diet on body weight.

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: Intervention trials that investigate the effect of a low GI/GL diet on body weight in humans will be included. Studies that are less than 12 weeks diet duration, do not have a control group, include pregnant women, breast feeding women or children, or do not report body weight data 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 body weight.

Data synthesis: Pooled analysis will be conducted using the Generic Inverse Variance method with random effects models 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. 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. A priori subgroup analyses will include GI/GL (absolute level, within-treatment change, and between-treatment change), comparator (high GI diet, other), duration of follow-up, saturated fat intake (absolute level, within-treatment change, and between-treatment change), dietary fibre intake (absolute level, within-treatment change, and between-treatment change), total carbohydrate intake (absolute level, within-treatment change, and between-treatment change), design (crossover, parallel), risk of bias, and median baseline body weight. Meta-regression analyses will assess the significance of categorical and continuous subgroups analyses. Publication bias will be investigated by the inspection of funnel plots and application of the Egger and Begg tests.

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.

Preliminary findings: We conducted a systematic review and meta-analysis of the effect of dietary pulses on glycemic control in 41 controlled feeding trials. We found that pulses alone or in low GI or high-fibre diets improved markers of glycemic control. Although the improvement was clinically significant, it came at the expense of substantial inter-study heterogeneity. We also conducted a systematic review and meta-analysis of prospective cohorts on the associations of GI and GL with coronary heart disease events. We found that high GI and GL diets were significantly associated with coronary heart disease events in women but not in men. Knowledge translation from these projects has already begun. These data have provided a rationale for a large trial of the effect of a low GI diet in type 2 diabetes to address some of the identified sources of heterogeneity, as well as the rationale for a trial of the effect of lowering the GL with canola oil on glycemic control and cardiovascular risk factors. These data were also used in the development of the 2013 CDA Clinical Practice Guidelines (CPG) for Nutrition Therapy.

Significance: The proposed project will aid in knowledge translation related to the effects of a low GI/GL diet on body weight, strengthening the evidence-base for dietary recommendations and health claims. It will improve health outcomes through informing healthcare providers and patients, stimulating industry innovation, and guiding future research.

Eligibility Criteria:
Inclusion Criteria:

* Dietary trials in humans
* Randomized trials
* Trials more or equals to 12 weeks duration
* Suitable control group
* Body weight data

Exclusion Criteria:

* Did not look at glycemic index or glycemic load
* Co-intervention (i.e. the study is designed in such a way that the effect of glycemic index or glycemic load can not be isolated)
* Trials looked at glycemic index of specific foods instead of diet as a whole
* Trials including pregnant or breast feeding women
* Trials including children
* Non-human studies
* Non-randomized trials
* Trials less than 12 weeks duration
* Lack of a suitable control group
* No viable endpoint data