Clinical trial NCT02654535: Meta-analyses of Nuts and Risk of Obesity
Official title: Nuts in Relation to Markers of Adiposity, Overweight, and Obesity: A Series of Systematic Reviews and Meta-analyses of Randomized Controlled Trials and Prospective Cohort Studies
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Body Weight
Obesity
Overweight
Adiposity
Obesity, Abdominal
Keywords: Systematic review and meta-analysis
Evidence-based medicine (EBM)
Evidence-based nutrition (EBN)
Clinical practice guidelines
Clinical trials
Tree Nuts
Peanuts
Body weight
Prospective cohort studies
Lead sponsor: University of Toronto
Sponsor class: OTHER
Collaborators: name: The Physicians' Services Incorporated Foundation
First posted: 2016-01-13
Enrollment: 1
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: All adults (\>=18 years), regardless of health status.

Brief summary: Peanuts and tree nuts (almonds, pistachios, walnuts, pecans, pine nuts, Brazil nuts, cashews, hazelnuts, macadamia nuts) (herein referred to as "nuts") are a good source of unsaturated fatty acids, vegetable protein, fibre, and polyphenolics. Nut intake has been associated with reduced cardiovascular disease risk and claims for this association have been permitted by the FDA; however, intake of tree nuts is low in Canada. One of the barriers to increasing the consumption of nuts is the perception that they may contribute to weight gain more than other "healthy foods" owing to their high energy density. The evidence supporting this concern, however, is lacking. In a series of earlier systematic reviews and meta-analyses, we have shown that nuts improve glycemic control and metabolic syndrome criteria, findings which run contrary to any expected weight gain. However, it remains unclear whether nuts have an increasing, neutral, or even decreasing effect on body weight. To address the uncertainties, the investigators propose to conduct a series of systematic reviews and meta-analyses of the totality of the evidence from randomized controlled trials and prospective cohort studies to investigate the effect of nut consumption on body weight and adiposity. The findings generated by this proposed knowledge synthesis will help improve the health of consumers through informing evidence-based guidelines and improving health outcomes by educating healthcare providers and patients, stimulating industry innovation, and guiding future research design

Detailed description: Background: Peanuts and tree nuts (almonds, Brazil nuts, cashews, hazelnuts, macadamia nuts, pecans, pine nuts, pistachios and walnuts) are an important source of unsaturated fatty acids, vegetable protein, and fibre, as well as minerals, vitamins, and phytonutrients. The FDA has permitted health claims for tree nuts for cardiovascular disease risk reduction and the cardiovascular benefits of nuts is acknowledged \[FDA, 2015; Bao et al., 2013; Sabate et al., 2010\]; however, intake of tree nuts is low in Canada. Based on the 2004 Canadian Community Health Survey (CCHS), \<5% of Canadians consumed nuts on any given day with a mean intake of 18 g/day in those consuming nuts \[PHAC, 2004\]. This intake level is far below the 42 g/day amount recommended by the FDA for cardiovascular risk reduction. One of the barriers to increasing the consumption of nuts is the perception that they may contribute to weight gain more than other "healthy foods" owing to their high energy density. With the rise in overweight and obesity and its downstream cardiometabolic complications, heart and diabetes associations have cautioned against the over consumption of nuts at the same time that they recommend them for heart health \[Sievenpiper et al., 2013; Evert et al., 2014; Anderson et al., 2013\]. In a series of earlier systematic reviews and meta-analyses, we have shown that nuts improve glycemic control and metabolic syndrome criteria, findings which run contrary to any expected weight gain \[Viguiliouk et al., 2014; Blanco Mejia et al., 2014\]. Although an earlier systematic review and meta-analysis of controlled trials showed a lack of effect of nut intake on body weight \[Flores-Mateo et al., 2013\], it remains unclear whether nuts have an increasing, neutral, or even decreasing effect on a broader set of markers of adiposity.

Need for proposed research:The lack of high quality syntheses and knowledge translation to reconcile the benefits of nuts with potential weight gain represents an urgent call for stronger evidence. High quality systematic reviews and meta-analyses of randomized controlled trials and prospective cohort studies represent the highest level of evidence to support dietary guidelines and public health policy development.

Objective: The investigators will conduct a series of systematic reviews and meta-analyses to (1) distinguish the effect of peanuts and tree nuts on body weight and markers of adiposity in randomized controlled trials and (2) assess peanut and tree nut consumption with incident overweight/obesity and changes in weight and markers of adiposity in prospective cohort studies.

Design: Each 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) \[Higgins et al., 2011; Moher et al., 2009\].

Data sources: MEDLINE, EMBASE, and The Cochrane Central Register of Controlled Trials (Clinical Trials; CENTRAL) will be searched using appropriate search terms supplemented by hand searches of references of included studies.

Study selection: The investigators will include either randomized controlled dietary trials or prospective cohort studies. Randomized controlled trials that investigate the effect of including and/or exchanging nuts for other nutrients on changes in body weight or markers of adiposity outcomes in adults (\>= 18 years) will be included. Studies that are \<3-weeks diet duration, lack a control, include individuals \<18 years, or assess intake during wasting conditions/malnourished populations, pregnancy or lactation will be excluded. Prospective cohort studies will be included if they are \>= 1-year duration, involving adults (\>=18 years) and assess the relation of tree nuts and/or peanuts with incident overweight/obesity or changes in body weight or markers of adiposity.

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: Three sets of outcomes will be assessed: (1) incidence of overweight/obesity, (2) measures of global adiposity (body weight, body mass index (BMI), body fat), (3) measures of abdominal adiposity (waist circumference, waist-to-hip ratio, visceral adipose tissue).

Data synthesis: Mean differences will be pooled for the trials and risk ratios for the cohorts 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 only be used where there is \<5 included studies. 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. If there are \>=10 studies, then the investigators will also explore sources of heterogeneity by a priori subgroup analyses by health status (metabolic syndrome/diabetes, overweight, normal weight), comparator (carbohydrate, other fat source, animal protein, mixed macronutrient, other), nut type, nut dose, baseline measurements, randomization, study design (parallel, crossover), energy balance (positive, neutral, negative), duration of follow-up, 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) \[Guyatt et al., 2011a, 2011b, 2011c, 2011d, 2011e, 2011f, 2011g, 2011h, 2011i; Balshem et al., 2011; Brunetti et al., 2013; Guyatt et al., 2013a, 2013b, 2013c\].

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 peanuts and tree nuts in relation to body weight, in particular adiposity and the development of overweight and obesity, strengthening the evidence-base for guidelines and improving health outcomes by educating healthcare providers and patients, stimulating industry innovation, and guiding future research design.

Eligibility criteria: Inclusion Criteria for randomized controlled trials:

* Trials in adults (\>=18 years)
* Tree nut and/or peanut intervention
* Presence of an adequate comparator (substitution, addition, subtraction, or ad libitum control)
* Diet duration \>=3 weeks
* viable outcome data

Inclusion Criteria for prospective cohort studies:

* Prospective cohort studies or case-cohort studies
* Duration \>= 1 year
* Assessing adults (\>=18 years)
* Assessment of the exposure of tree nuts and/or peanuts
* Ascertainment of viable data by level of exposure

Exclusion Criteria for randomized controlled trials:

* non-human trials
* assessing individuals \<18 years
* observational studies
* lack of suitable comparator diet (i.e. a comparator arm that contains substantial amounts of tree nuts or peanuts)
* Diet duration \<3-weeks
* No viable outcome data

Exclusion Criteria for prospective cohort studies:

* Ecological, cross-sectional, and retrospective observational studies, clinical trials, and non-human studies
* Duration \< 1 year
* assessing individuals \<18 years
* No assessment of exposures of tree nuts and/or peanuts
* No ascertainment viable clinical outcome data by level of exposure

Primary outcomes:
measure: Incident overweight or obesity (prospective cohort studies)
timeFrame: Up to 40 years
measure: Body weight (randomized controlled trials)
timeFrame: Up to 40 years
Secondary outcomes:
measure: Global measures of adiposity with established clinical relevance - body weight (prospective cohort studies)
timeFrame: Up to 40 years
measure: Global measures of adiposity with established clinical relevance - BMI (prospective cohort studies and randomized controlled trials)
timeFrame: Up to 40 years
measure: Global measures of adiposity with established clinical relevance - body fat (prospective cohort studies and randomized controlled trials)
timeFrame: Up to 40 years
measure: Regional measures of adiposity with established clinical relevance - waist circumference (prospective cohort studies and randomized controlled trials)
timeFrame: Up to 40 years
measure: Regional measures of adiposity with established clinical relevance - waist-to-hip ratio (prospective cohort studies and randomized controlled trials)
timeFrame: Up to 40 years
measure: Regional measures of adiposity with established clinical relevance - visceral adipose tissue (prospective cohort studies and randomized controlled trials)
timeFrame: Up to 40 years
Other outcomes:
Locations:
city: Toronto
country: Canada
facility: The Toronto 3D (Diet, Digestive tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael's Hospital
state: Ontario
Study officials:
affiliation: University of Toronto
name: John L Sievenpiper, MD, PhD, FRCPC
role: PRINCIPAL_INVESTIGATOR
References:
citation: U.S. Food and Drug Administration (FDA). Guidance for Industry: A Food Labeling Guide (12. Appendix D: Qualified Health Claims). Available at: http://www.fda.gov/Food/GuidanceRegulation/GuidanceDocumentsRegulartoyInformation/LabelingNutrition/ucm064923.htm (Page Last Updated: 08/20/2015).
citation: Bao Y, Han J, Hu FB, Giovannucci EL, Stampfer MJ, Willett WC, Fuchs CS. Association of nut consumption with total and cause-specific mortality. N Engl J Med. 2013 Nov 21;369(21):2001-11. doi: 10.1056/NEJMoa1307352.
citation: Sabate J, Oda K, Ros E. Nut consumption and blood lipid levels: a pooled analysis of 25 intervention trials. Arch Intern Med. 2010 May 10;170(9):821-7. doi: 10.1001/archinternmed.2010.79.
citation: Public Health Agency of Canada (PHAC). National Single Day Food Consumption Report: Analysis of the 24-hour dietary recall data from the Canadian Community Health Survey (CCHS), Cycle 2.2, Nutrition (2004), and assessment for food consumption frequency among Canadians. Available at: http://www.phac-aspc.gc.ca.
citation: Sievenpiper JL, Dworatzek PD. Food and dietary pattern-based recommendations: an emerging approach to clinical practice guidelines for nutrition therapy in diabetes. Can J Diabetes. 2013 Feb;37(1):51-7. doi: 10.1016/j.jcjd.2012.11.001. Epub 2013 Mar 14.
citation: Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, Neumiller JJ, Nwankwo R, Verdi CL, Urbanski P, Yancy WS Jr. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2014 Jan;37 Suppl 1:S120-43. doi: 10.2337/dc14-S120. No abstract available.
citation: Anderson TJ, Gregoire J, Hegele RA, Couture P, Mancini GB, McPherson R, Francis GA, Poirier P, Lau DC, Grover S, Genest J Jr, Carpentier AC, Dufour R, Gupta M, Ward R, Leiter LA, Lonn E, Ng DS, Pearson GJ, Yates GM, Stone JA, Ur E. 2012 update of the Canadian Cardiovascular Society guidelines for the diagnosis and treatment of dyslipidemia for the prevention of cardiovascular disease in the adult. Can J Cardiol. 2013 Feb;29(2):151-67. doi: 10.1016/j.cjca.2012.11.032.
citation: Viguiliouk E, Kendall CW, Blanco Mejia S, Cozma AI, Ha V, Mirrahimi A, Jayalath VH, Augustin LS, Chiavaroli L, Leiter LA, de Souza RJ, Jenkins DJ, Sievenpiper JL. Effect of tree nuts on glycemic control in diabetes: a systematic review and meta-analysis of randomized controlled dietary trials. PLoS One. 2014 Jul 30;9(7):e103376. doi: 10.1371/journal.pone.0103376. eCollection 2014.
citation: Blanco Mejia S, Kendall CW, Viguiliouk E, Augustin LS, Ha V, Cozma AI, Mirrahimi A, Maroleanu A, Chiavaroli L, Leiter LA, de Souza RJ, Jenkins DJ, Sievenpiper JL. Effect of tree nuts on metabolic syndrome criteria: a systematic review and meta-analysis of randomised controlled trials. BMJ Open. 2014 Jul 29;4(7):e004660. doi: 10.1136/bmjopen-2013-004660.
citation: Higgins JPT, Greens S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. The Cochrane Collaboration, 2011. Available from www.cochrane-handbook.org.
citation: Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009 Jul 21;339:b2535. doi: 10.1136/bmj.b2535.
citation: Guyatt GH, Oxman AD, Schunemann HJ, Tugwell P, Knottnerus A. GRADE guidelines: a new series of articles in the Journal of Clinical Epidemiology. J Clin Epidemiol. 2011 Apr;64(4):380-2. doi: 10.1016/j.jclinepi.2010.09.011. Epub 2010 Dec 24.
citation: Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y, Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schunemann HJ. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011 Apr;64(4):383-94. doi: 10.1016/j.jclinepi.2010.04.026. Epub 2010 Dec 31.
citation: Guyatt GH, Oxman AD, Kunz R, Atkins D, Brozek J, Vist G, Alderson P, Glasziou P, Falck-Ytter Y, Schunemann HJ. GRADE guidelines: 2. Framing the question and deciding on important outcomes. J Clin Epidemiol. 2011 Apr;64(4):395-400. doi: 10.1016/j.jclinepi.2010.09.012. Epub 2010 Dec 30.
citation: Balshem H, Helfand M, Schunemann HJ, Oxman AD, Kunz R, Brozek J, Vist GE, Falck-Ytter Y, Meerpohl J, Norris S, Guyatt GH. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011 Apr;64(4):401-6. doi: 10.1016/j.jclinepi.2010.07.015. Epub 2011 Jan 5.
citation: Guyatt GH, Oxman AD, Vist G, Kunz R, Brozek J, Alonso-Coello P, Montori V, Akl EA, Djulbegovic B, Falck-Ytter Y, Norris SL, Williams JW Jr, Atkins D, Meerpohl J, Schunemann HJ. GRADE guidelines: 4. Rating the quality of evidence--study limitations (risk of bias). J Clin Epidemiol. 2011 Apr;64(4):407-15. doi: 10.1016/j.jclinepi.2010.07.017. Epub 2011 Jan 19.
citation: Guyatt GH, Oxman AD, Montori V, Vist G, Kunz R, Brozek J, Alonso-Coello P, Djulbegovic B, Atkins D, Falck-Ytter Y, Williams JW Jr, Meerpohl J, Norris SL, Akl EA, Schunemann HJ. GRADE guidelines: 5. Rating the quality of evidence--publication bias. J Clin Epidemiol. 2011 Dec;64(12):1277-82. doi: 10.1016/j.jclinepi.2011.01.011. Epub 2011 Jul 30.
citation: Guyatt GH, Oxman AD, Kunz R, Brozek J, Alonso-Coello P, Rind D, Devereaux PJ, Montori VM, Freyschuss B, Vist G, Jaeschke R, Williams JW Jr, Murad MH, Sinclair D, Falck-Ytter Y, Meerpohl J, Whittington C, Thorlund K, Andrews J, Schunemann HJ. GRADE guidelines 6. Rating the quality of evidence--imprecision. J Clin Epidemiol. 2011 Dec;64(12):1283-93. doi: 10.1016/j.jclinepi.2011.01.012. Epub 2011 Aug 11.
citation: Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, Alonso-Coello P, Glasziou P, Jaeschke R, Akl EA, Norris S, Vist G, Dahm P, Shukla VK, Higgins J, Falck-Ytter Y, Schunemann HJ; GRADE Working Group. GRADE guidelines: 7. Rating the quality of evidence--inconsistency. J Clin Epidemiol. 2011 Dec;64(12):1294-302. doi: 10.1016/j.jclinepi.2011.03.017. Epub 2011 Jul 31.
citation: Guyatt GH, Oxman AD, Kunz R, Woodcock J, Brozek J, Helfand M, Alonso-Coello P, Falck-Ytter Y, Jaeschke R, Vist G, Akl EA, Post PN, Norris S, Meerpohl J, Shukla VK, Nasser M, Schunemann HJ; GRADE Working Group. GRADE guidelines: 8. Rating the quality of evidence--indirectness. J Clin Epidemiol. 2011 Dec;64(12):1303-10. doi: 10.1016/j.jclinepi.2011.04.014. Epub 2011 Jul 30.
citation: Guyatt GH, Oxman AD, Sultan S, Glasziou P, Akl EA, Alonso-Coello P, Atkins D, Kunz R, Brozek J, Montori V, Jaeschke R, Rind D, Dahm P, Meerpohl J, Vist G, Berliner E, Norris S, Falck-Ytter Y, Murad MH, Schunemann HJ; GRADE Working Group. GRADE guidelines: 9. Rating up the quality of evidence. J Clin Epidemiol. 2011 Dec;64(12):1311-6. doi: 10.1016/j.jclinepi.2011.06.004. Epub 2011 Jul 30.
citation: Brunetti M, Shemilt I, Pregno S, Vale L, Oxman AD, Lord J, Sisk J, Ruiz F, Hill S, Guyatt GH, Jaeschke R, Helfand M, Harbour R, Davoli M, Amato L, Liberati A, Schunemann HJ. GRADE guidelines: 10. Considering resource use and rating the quality of economic evidence. J Clin Epidemiol. 2013 Feb;66(2):140-50. doi: 10.1016/j.jclinepi.2012.04.012. Epub 2012 Aug 3.
citation: Guyatt G, Oxman AD, Sultan S, Brozek J, Glasziou P, Alonso-Coello P, Atkins D, Kunz R, Montori V, Jaeschke R, Rind D, Dahm P, Akl EA, Meerpohl J, Vist G, Berliner E, Norris S, Falck-Ytter Y, Schunemann HJ. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. J Clin Epidemiol. 2013 Feb;66(2):151-7. doi: 10.1016/j.jclinepi.2012.01.006. Epub 2012 Apr 27.
citation: Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, Brozek J, Norris S, Meerpohl J, Djulbegovic B, Alonso-Coello P, Post PN, Busse JW, Glasziou P, Christensen R, Schunemann HJ. GRADE guidelines: 12. Preparing summary of findings tables-binary outcomes. J Clin Epidemiol. 2013 Feb;66(2):158-72. doi: 10.1016/j.jclinepi.2012.01.012. Epub 2012 May 18.
citation: Guyatt GH, Thorlund K, Oxman AD, Walter SD, Patrick D, Furukawa TA, Johnston BC, Karanicolas P, Akl EA, Vist G, Kunz R, Brozek J, Kupper LL, Martin SL, Meerpohl JJ, Alonso-Coello P, Christensen R, Schunemann HJ. GRADE guidelines: 13. Preparing summary of findings tables and evidence profiles-continuous outcomes. J Clin Epidemiol. 2013 Feb;66(2):173-83. doi: 10.1016/j.jclinepi.2012.08.001. Epub 2012 Oct 30.
citation: Flores-Mateo G, Rojas-Rueda D, Basora J, Ros E, Salas-Salvado J. Nut intake and adiposity: meta-analysis of clinical trials. Am J Clin Nutr. 2013 Jun;97(6):1346-55. doi: 10.3945/ajcn.111.031484. Epub 2013 Apr 17.
citation: Nishi SK, Viguiliouk E, Blanco Mejia S, Kendall CWC, Bazinet RP, Hanley AJ, Comelli EM, Salas Salvado J, Jenkins DJA, Sievenpiper JL. Are fatty nuts a weighty concern? A systematic review and meta-analysis and dose-response meta-regression of prospective cohorts and randomized controlled trials. Obes Rev. 2021 Nov;22(11):e13330. doi: 10.1111/obr.13330. Epub 2021 Sep 8.