Effect of Incorporating Genetic Testing Results into Nutrition Counseling and Care on Dietary Intake: An Evidence Analysis Center Systematic Review—Part I


      Consumer interest in personalized nutrition based on nutrigenetic testing is growing. Recently, multiple, randomized controlled trials have sought to understand whether incorporating genetic information into dietary counseling alters dietary outcomes. The objective of this systematic review was to examine how incorporating genetic information into nutrition counseling and care, compared to an alternative intervention or control group, impacts dietary outcomes. This is the first of a 2-part systematic review series. Part II reports anthropometric, biochemical, and disease-specific outcomes. Peer-reviewed randomized controlled trials were identified through a systematic literature search of multiple databases, screened for eligibility, and critically reviewed and synthesized. Conclusion statements were graded to determine quality of evidence for each dietary outcome reported. Reported outcomes include intake of total energy and macronutrients, micronutrients, foods, food groups, food components (added sugar, caffeine, and alcohol), and composite diet scores. Ten articles representing 8 unique randomized controlled trials met inclusion criteria. Of 15 conclusion statements (evidence grades: Weak to Moderate), 13 concluded there was no significant effect of incorporating genetic information into nutrition counseling/care on dietary outcomes. Limited data suggested that carriers of higher-risk gene variants were more likely than carriers of low-risk gene variants to significantly reduce intake of sodium and alcohol in response to nutrition counseling that incorporated genetic results. Included studies differed in quality, selected genetic variants, timing and intensity of intervention, sample size, dietary assessment tools, and population characteristics. Therefore, strong conclusions could not be drawn. Collaboration between the Academy of Nutrition and Dietetics and professional nutrigenetic societies would likely prove valuable in prioritizing which genetic variants and targeted nutrition messages have the most potential to alter dietary outcomes in a given patient subpopulation and, thus, should be the targets of future research.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Journal of the Academy of Nutrition and Dietetics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Camp K.M.
        • Trujillo E.
        Position of the Academy of Nutrition and Dietetics: Nutritional genomics.
        J Acad Nutr Diet. 2014; 114: 299-312
        • Regalado A.
        More than 26 million people have taken an at-home ancestry test. MIT Technology Review.
        (Published February 11, 2019. Accessed August 10, 2019)
        • Guo F.
        • Hirth J.M.
        • Lin Y.L.
        • et al.
        Use of BRCA mutation test in the US, 2004–2014.
        Am J Prev Med. 2017; 52: 702-709
        • Nielsen D.E.
        • Shih S.
        • El-Sohemy A.
        Perceptions of genetic testing for personalized nutrition: A randomized trial of DNA-based dietary advice.
        Lifestyle Genom. 2014; 7: 94-104
        • Marcotte B.V.
        • Cormier H.
        • Garneau V.
        • Robitaille J.
        • Desroches S.
        • Vohl M.C.
        Nutrigenetic testing for personalized nutrition: An evaluation of public perceptions, attitudes, and concerns in a population of French Canadians.
        Lifestyle Genom. 2018; 11: 155-162
        • Bray G.A.
        • Krauss R.M.
        • Sacks F.M.
        • Qi L.
        Lessons learned from the POUNDS Lost Study: Genetic, metabolic, and behavioral factors affecting changes in body weight, body composition, and cardiometabolic risk.
        Curr Obes Rep. 2019; 8: 262-283
        • Rozga M.
        • Handu D.
        Nutritional genomics in precision nutrition: An evidence analysis center scoping review.
        J Acad Nutr Diet. 2019; 119: 507-515
        • Handu D.
        • Moloney L.
        • Wolfram T.
        • Ziegler P.
        • Acosta A.
        • Steiber A.
        Academy of Nutrition and Dietetics methodology for conducting systematic reviews for the Evidence Analysis Library.
        J Acad Nutr Diet. 2016; 116: 311-318
        • Moher D.
        • Liberati A.
        • Tetzlaff J.
        • Altman D.G.
        Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA statement.
        Ann Intern Med. 2009; 151: 264-269
        • Rozga M.
        • Vargas A.
        • Ellis A.
        • Braakhuis A.
        • Monnard C.
        • Robinson K.
        Use of genetic information in precision nutrition care: A systematic review. PROSPERO 2019 CRD42019119953.
      1. Higgins J.P.T. Thomas J. Chandler J. Cochrane Handbook for Systematic Reviews of Interventions. Version 6.0. Cochrane, 2019, 2019 ( Updated July 2019. Accessed May 18, 2020)
      2. Canadian Agency for Drugs and Technologies in Health (CADTH). Strings attached: CADTH's database search filters.
        (Published April 29, 2019. Accessed May 18, 2020)
        • Ouzzani M.
        • Hammady H.
        • Fedorowicz Z.
        • Elmagarmid A.
        Rayyan-a web and mobile app for systematic reviews.
        Syst Rev. 2016; 5: 210
        • Evidence Analysis Center, Academy of Nutrition and Dietetics
        Evidence Analysis Manual: Steps in the Academy Evidence Analysis Process.
        Academy of Nutrition and Dietetics, Chicago, IL2016
        • Wallace B.C.
        • Dahabreh I.J.
        • Trikalinos T.A.
        • Lau J.
        • Trow P.
        • Schmid C.H.
        Closing the gap between methodologists and end-users: R as a computational back-end.
        J Stat Softw. 2012; 49
        • Team R.
        RStudio: Integrated development for R.
        (Accessed May 18, 2020)
        • Guyatt G.
        • Oxman A.D.
        • Akl E.A.
        • et al.
        GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables.
        J Clin Epidemiol. 2011; 64: 383-394
        • Celis-Morales C.
        • Livingstone K.M.
        • Marsaux C.F.
        • et al.
        Effect of personalized nutrition on health-related behaviour change: Evidence from the Food4Me European randomized controlled trial.
        Int J Epidemiol. 2017; 46: 578-588
        • Fallaize R.
        • Celis-Morales C.
        • Macready A.L.
        • et al.
        The effect of the apolipoprotein E genotype on response to personalized dietary advice intervention: Findings from the Food4Me randomized controlled trial.
        Am J Clin Nutr. 2016; 104: 827-836
        • Godino J.G.
        • van Sluijs E.M.
        • Marteau T.M.
        • Sutton S.
        • Sharp S.J.
        • Griffin S.J.
        Lifestyle advice combined with personalized estimates of genetic or phenotypic risk of type 2 diabetes, and objectively measured physical activity: A randomized controlled trial.
        PLoS Med. 2016; 13e1002185
        • Hendershot C.S.
        • Otto J.M.
        • Collins S.E.
        • Liang T.
        • Wall T.L.
        Evaluation of a brief web-based genetic feedback intervention for reducing alcohol-related health risks associated with ALDH2.
        Ann Behav Med. 2010; 40: 77-88
        • Kullo I.J.
        • Jouni H.
        • Austin E.E.
        • et al.
        Incorporating a genetic risk score into coronary heart disease risk estimates: Effect on low-density lipoprotein cholesterol Levels (the MI-GENES Clinical Trial).
        Circulation. 2016; 133: 1181-1188
        • Livingstone K.M.
        • Celis-Morales C.
        • Navas-Carretero S.
        • et al.
        Food4Me Study: Effect of an internet-based, personalized nutrition randomized trial on dietary changes associated with the Mediterranean diet: The Food4Me Study.
        Am J Clin Nutr. 2016; 104: 288-297
        • Nielsen D.E.
        • El-Sohemy A.
        Disclosure of genetic information and change in dietary intake: A randomized controlled trial.
        PLoS One. 2014; 9e112665
        • Roke K.
        Exploration of the perceived and actual benefits of omega-3 fatty acids and the impact of FADS1 and FADS2 genetic information on dietary intake and blood levels of EPA and DHA.
        Appl Physiol Nutr Metab. 2017; 42: 333
        • Samaan Z.
        • Schulze K.M.
        • Middleton C.
        • et al.
        South Asian Heart Risk Assessment (SAHARA): Randomized controlled trial design and pilot study.
        JMIR Res Protoc. 2013; 2: e33
        • Voils C.I.
        • Coffman C.J.
        • Grubber J.M.
        • et al.
        Does type 2 diabetes genetic testing and counseling reduce modifiable risk factors? A randomized controlled trial of veterans.
        J Gen Intern Med. 2015; 30: 1591-1598
        • Cormier H.
        • Tremblay B.L.
        • Paradis A.M.
        • et al.
        Nutrigenomics—perspectives from registered dietitians: A report from the Quebec-wide e-consultation on nutrigenomics among registered dietitians.
        J Hum Nutr Diet. 2014; 27: 391-400
        • Li S.X.
        • Ye Z.
        • Whelan K.
        • Truby H.
        The effect of communicating the genetic risk of cardiometabolic disorders on motivation and actual engagement in preventative lifestyle modification and clinical outcome: A systematic review and meta-analysis of randomised controlled trials.
        Br J Nutr. 2016; 116: 924-934
        • Hollands G.J.
        • French D.P.
        • Griffin S.J.
        • et al.
        The impact of communicating genetic risks of disease on risk-reducing health behaviour: Systematic review with meta-analysis.
        Br Med J. 2016; 352: i1102
        • Horne J.
        • Madill J.
        • O’Connor C.
        • Shelley J.
        • Gilliland J.
        A systematic review of genetic testing and lifestyle behaviour change: Are we using high-quality genetic interventions and considering behaviour change theory?.
        Lifestyle Genom. 2018; 11: 49-63
        • Stewart-Knox B.J.
        • Bunting B.P.
        • Gilpin S.
        • et al.
        Attitudes toward genetic testing and personalised nutrition in a representative sample of European consumers.
        Br J Nutr. 2009; 101: 982-989
        • Marcotte B.V.
        • Cormier H.
        • Garneau V.
        • Robitaille J.
        • Desroches S.
        • Vohl M.C.
        Current knowledge and interest of French Canadians regarding nutrigenetics.
        Genes Nutr. 2019; 14: 5
        • Stewart K.F.
        • Wesselius A.
        • Schreurs M.A.
        • Schols A.M.
        • Zeegers M.P.
        Behavioural changes, sharing behaviour and psychological responses after receiving direct-to-consumer genetic test results: A systematic review and meta-analysis.
        J Community Genet. 2018; 9: 1-8
        • Glanz K.
        • Bishop D.B.
        The role of behavioral science theory in development and implementation of public health interventions.
        Ann Rev Public Health. 2010; 31: 399-418
        • Harvie M.
        • Pegington M.
        • French D.
        • et al.
        Breast cancer risk status influences uptake, retention and efficacy of a weight loss programme amongst breast cancer screening attendees: Two randomised controlled feasibility trials.
        BMC Cancer. 2019; 19: 1089
        • Grimaldi K.A.
        • van Ommen B.
        • Ordovas J.M.
        • et al.
        Proposed guidelines to evaluate scientific validity and evidence for genotype-based dietary advice.
        Genes Nutr. 2017; 12: 35
        • Zeevi D.
        • Korem T.
        • Zmora N.
        • et al.
        Personalized nutrition by prediction of glycemic responses.
        Cell. 2015; 163: 1079-1094
        • Spector T.
        Predicting personal metabolic responses to food using multi-omics machine learning in over 1000 twins and singletons from the UK and US: The PREDICT 1 Study.


      K. Robinson is a medical science liaison, Scientific and Medical Affairs, Abbott Nutrition, Columbus, OH; at the time of the study, she was a postdoctoral researcher, Department of Internal Medicine, University of Iowa, Iowa City.


      M. Rozga is a nutrition researcher, Academy of Nutrition and Dietetics, Evidence Analysis Center, Chicago, IL.


      A. Braakhuis is an academic director, Faculty of Medical and Health Science, Discipline of Nutrition, The University of Auckland, Grafton, Auckland, New Zealand.


      A. Ellis is an associate professor, University of Alabama, Tuscaloosa.


      C. R. Monnard is a specialist in infant nutrition, Société des Produits Nestlé SA, Nestlé Research, Lausanne, Switzerland; at the time of the study, she was a postdoctoral fellow, University of Fribourg, Fribourg, Switzerland.


      R. Sinley is an assistant professor, Metropolitan State University of Denver, Denver, CO.


      A. Wanner is an information specialist consultant, Kelowna, British Columbia, Canada.


      A. J. Vargas is a scientist, National Institutes of Health, Office of Disease Prevention, Rockville, MD.

      Linked Article

      • Response to the Consensus Report of the Academy of Nutrition and Dietetics: Incorporating Genetic Testing into Nutrition Care
        Journal of the Academy of Nutrition and DieteticsVol. 120Issue 12
        • Preview
          It is our pleasure to engage with the Academy of Nutrition and Dietetics in this important discussion on the impact of incorporating genetic testing into nutrition care. We reviewed, with interest, the Academy’s Consensus Report and companion papers detailing the conducted scoping and systematic reviews on the impact of genetic testing on dietary change and health outcomes.1-4 This work is of great importance, and we applaud the authors for encouraging registered dietitians to become involved in the research process1 in an effort to optimize pragmatic research in precision nutrition.
        • Full-Text
        • PDF