- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT02961088
Meta-analyses of Pasta as Part of Low Glycemic Index Diets and Adiposity
Effect of Pasta as Part of Low Glycemic Index Diets on Body Weight and Markers of Adiposity in Humans: A Series of Systematic Reviews and Meta-analyses to Provide Evidence-based Guidance for Nutrition Guidelines Development
Study Overview
Status
Intervention / Treatment
Detailed Description
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.
Study Type
Enrollment (Anticipated)
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Dietary trials in humans
- Randomized trials
- Trials greater than or equal to 3 weeks duration
- Suitable control group
- Viable outcome data
Exclusion Criteria:
- No assessment of the glycemic index
- Co-intervention present (i.e. the study is designed in such a way that the effect of past as part of a low glycemic index or glycemic load diet can not be isolated)
- Trials looking at the glycemic index of specific foods instead of the diet as a whole
- Trials that did not include pasta as part of the low GI/GL diet
- Trials that included pasta in the control diet
- Trials including pregnant or breast feeding women
- Non-human studies
- Non-randomized trials
- Trials less than 3 weeks duration
- Lack of a suitable control group
- No viable endpoint data
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Global measures of adiposity with established clinical relevance - body weight
Time Frame: Up to 20 years
|
body weight
|
Up to 20 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Global measures of adiposity with established clinical relevance - BMI
Time Frame: Up to 20 years
|
body mass index, BMI
|
Up to 20 years
|
Global measures of adiposity with established clinical relevance - percentage body fat
Time Frame: Up to 20 years
|
percentage body fat
|
Up to 20 years
|
Regional measures of adiposity with established clinical relevance - waist circumference
Time Frame: Up to 20 years
|
waist circumference
|
Up to 20 years
|
Regional measures of adiposity with established clinical relevance - waist-to-hip ratio
Time Frame: Up to 20 years
|
waist-to-hip ratio
|
Up to 20 years
|
Regional measures of adiposity with established clinical relevance - sagittal abdominal diameter
Time Frame: Up to 20 years
|
sagittal abdominal diameter
|
Up to 20 years
|
Measures of adiposity by established imaging techniques - visceral adipose tissue [VAT]
Time Frame: Up to 20 years
|
visceral adipose tissue [VAT] by imaging modalities
|
Up to 20 years
|
Collaborators and Investigators
Sponsor
Publications and helpful links
General Publications
- Larsen TM, Dalskov SM, van Baak M, Jebb SA, Papadaki A, Pfeiffer AF, Martinez JA, Handjieva-Darlenska T, Kunesova M, Pihlsgard M, Stender S, Holst C, Saris WH, Astrup A; Diet, Obesity, and Genes (Diogenes) Project. Diets with high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med. 2010 Nov 25;363(22):2102-13. doi: 10.1056/NEJMoa1007137.
- Jenkins DJ, Kendall CW, Augustin LS, Mitchell S, Sahye-Pudaruth S, Blanco Mejia S, Chiavaroli L, Mirrahimi A, Ireland C, Bashyam B, Vidgen E, de Souza RJ, Sievenpiper JL, Coveney J, Leiter LA, Josse RG. Effect of legumes as part of a low glycemic index diet on glycemic control and cardiovascular risk factors in type 2 diabetes mellitus: a randomized controlled trial. Arch Intern Med. 2012 Nov 26;172(21):1653-60. doi: 10.1001/2013.jamainternmed.70.
- Mirrahimi A, de Souza RJ, Chiavaroli L, Sievenpiper JL, Beyene J, Hanley AJ, Augustin LS, Kendall CW, Jenkins DJ. Associations of glycemic index and load with coronary heart disease events: a systematic review and meta-analysis of prospective cohorts. J Am Heart Assoc. 2012 Oct;1(5):e000752. doi: 10.1161/JAHA.112.000752. Epub 2012 Oct 25.
- Turner-McGrievy GM, Jenkins DJ, Barnard ND, Cohen J, Gloede L, Green AA. Decreases in dietary glycemic index are related to weight loss among individuals following therapeutic diets for type 2 diabetes. J Nutr. 2011 Aug;141(8):1469-74. doi: 10.3945/jn.111.140921. Epub 2011 Jun 8.
- Chiavaroli L, Kendall CWC, Braunstein CR, Blanco Mejia S, Leiter LA, Jenkins DJA, Sievenpiper JL. Effect of pasta in the context of low-glycaemic index dietary patterns on body weight and markers of adiposity: a systematic review and meta-analysis of randomised controlled trials in adults. BMJ Open. 2018 Apr 2;8(3):e019438. doi: 10.1136/bmjopen-2017-019438.
Study record dates
Study Major Dates
Study Start
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Estimate)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- Toronto3D Pasta 2016
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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