Meta-analysis of the Portfolio Dietary Pattern and Cardiometabolic Risk

Background: The Portfolio Dietary Pattern, which includes nuts, viscous fiber, plant protein, and plant sterols, has been shown to have a cholesterol-lowering effect similar to that of early statin medications. Despite the endorsement of the Portfolio Dietary Pattern by major international diabetes and cardiovascular guidelines, the European Association for the Study of Diabetes (EASD) guidelines for nutrition therapy have not made any specific recommendations for the Portfolio Dietary Pattern. The present systematic review and meta-analysis using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was thus commissioned by the Diabetes and Nutrition Study Group (DNSG) of the EASD to summarize the available evidence from controlled trials of the effect of the Portfolio dietary pattern on LDL-cholesterol (LDL-C) and other established cardiometabolic risk factors.

Need for proposed research: High quality systematic reviews and meta-analyses of controlled trials represent the highest level of evidence to support dietary guidelines and public health policy development. As dietary guidelines and public health policy have shifted toward food and dietary-pattern based recommendations, there is a need for a systematic review and meta-analysis assessing the pooled effect of the Portfolio Dietary Pattern trials on cholesterol and cardiometabolic risk factors.

Objective: The investigators will conduct a systematic review and meta-analysis to summarize the effect of a Portfolio Dietary Pattern compared to control on lipids and other cardiometabolic risk factors in controlled clinical trials.

Design: The 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 will be searched using appropriate search terms supplemented by hand searches of references of included studies. Authors will be contacted for applicable missing data.

Study selection: The investigators will include controlled dietary trials. Randomized and non-randomized controlled trials will be included if they are >= 4 weeks duration and assess the effect of the Portfolio Dietary Pattern on cholesterol and cardiometabolic risk factors compared to a control diet.

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: The primary outcome of the systematic review and meta-analysis will be the established lipid target for cardiovascular risk reduction, LDL-C. Secondary outcomes will include other blood lipids (total-C, triglycerides, HDL-C, non-HDL-C, apolipoprotein B (apo B)), adiposity (body weight), inflammation (C-reactive protein), blood pressure (systolic and diastolic blood pressure (BP)), glycemic control (HbA1c, fasting plasma glucose, fasting blood insulin), and the Framingham risk score for the estimation of 10 year coronary heart disease (CHD) risk.

Data synthesis: Mean differences and standardized mean differences will be pooled for the outcomes noted using the generic inverse variance method. 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 be considered where there are <5 included studies and large precise trials are being combined with smaller imprecise trials. 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. A sensitivity analysis will also be conducted comparing trials where food was provided with trials where participants received dietary advice, but not provided food. If there are >=10 studies, then the investigators will also explore sources of heterogeneity by a priori subgroup analyses by age (children [=<18 years of age], adults), health status (metabolic syndrome/diabetes, overweight, normal weight), comparator, baseline measurements, randomization, study design (parallel, crossover), follow-up (=<8-weeks, >8-weeks), and risk of bias. Meta-regression analyses will assess the significance of categorical and continuous subgroups analyses. 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, 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 strength of the evidence for each outcome will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (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 related to the role of the Portfolio Dietary Pattern in cholesterol-lowering and cardiometabolic risk, strengthening the evidence-base for guidelines and improving health outcomes by educating healthcare providers and patients, stimulating industry innovation, and guiding future research design.