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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

  • Sherly X. Li (a1), Zheng Ye (a1), Kevin Whelan (a2) and Helen Truby (a3)

Genetic risk prediction of chronic conditions including obesity, diabetes and CVD currently has limited predictive power but its potential to engage healthy behaviour change has been of immense research interest. We aimed to understand whether the latter is indeed true by conducting a systematic review and meta-analysis investigating whether genetic risk communication affects motivation and actual behaviour change towards preventative lifestyle modification. We included all randomised controlled trials (RCT) since 2003 investigating the impact of genetic risk communication on health behaviour to prevent cardiometabolic disease, without restrictions on age, duration of intervention or language. We conducted random-effects meta-analyses for perceived motivation for behaviour change and clinical changes (weight loss) and a narrative analysis for other outcomes. Within the thirteen studies reviewed, five were vignette studies (hypothetical RCT) and seven were clinical RCT. There was no consistent effect of genetic risk on actual motivation for weight loss, perceived motivation for dietary change (control v. genetic risk group standardised mean difference (smd) −0·15; 95 % CI −1·03, 0·73, P=0·74) or actual change in dietary behaviour. Similar results were observed for actual weight loss (control v. high genetic risk SMD 0·29 kg; 95 % CI −0·74, 1·31, P=0·58). This review found no clear or consistent evidence that genetic risk communication alone either raises motivation or translates into actual change in dietary intake or physical activity to reduce the risk of cardiometabolic disorders in adults. Of thirteen studies, eight were at high or unclear risk of bias. Additional larger-scale, high-quality clinical RCT are warranted.

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This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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* Corresponding author: S. X. Li, email
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1. Gibney, MJ & Walsh, MC (2013) The future direction of personalised nutrition: my diet, my phenotype, my genes. Proc Nutr Soc 72, 219225.
2. Franks, PW, Pearson, E & Florez, JC (2013) Gene-environment and gene-treatment interactions in type 2 diabetes: progress, pitfalls, and prospects. Diabetes Care 36, 14131421.
3. Nielsen, DE & El-Sohemy, A (2014) Disclosure of genetic information and change in dietary intake: a randomized controlled trial. PLOS ONE 9, e112665.
4. McBride, CM, Koehly, LM, Sanderson, SC, et al. (2010) The behavioral response to personalized genetic information: will genetic risk profiles motivate individuals and families to choose more healthful behaviors? Annu Rev Public Health 31, 89103.
5. Grant, RW, Hivert, M, Pandiscio, JC, et al. (2009) The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia 52, 22992305.
6. Strecher, VJ & Rosenstock, IM (1997) The health belief model. In Cambridge Handbook of Psychology, Health and Medicine, pp. 113117 [A Baum, S Newman, C McManus and K Wallston, editors]. Cambridge, MA: Cambridge University Press.
7. Saukko, P (2013) State of play in direct-to-consumer genetic testing for lifestyle-related diseases: market, marketing content, user experiences and regulation. Proc Nutr Soc 72, 5360.
8. Fallaize, R, Macready, AL, Butler, LT, et al. (2013) An insight into the public acceptance of nutrigenomic-based personalised nutrition. Nutr Res Rev 26, 3948.
9. Schneider, KI & Schmidtke, J (2014) Patient compliance based on genetic medicine: a literature review. J Community Genet 5, 3148.
10. Marteau, TM, French, DP, Griffin, SJ, et al. (2010) Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours. The Cochrane Database of Systematic Reviews 10, 1617.
11. Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group (2013) Recommendations from the EGAPP Working Group: does genomic profiling to assess type 2 diabetes risk improve health outcomes? Genet Med 15, 612617.
12. Bloss, CS, Madlensky, L, Schork, NJ, et al. (2011) Genomic information as a behavioral health intervention: can it work? Pers Med 8, 659667.
13. Vernarelli, JA (2013) Impact of genetic risk assessment on nutrition-related lifestyle behaviours. Proc Nutr Soc 72, 153159.
14. Bloss, CS, Schork, NJ & Topol, EJ (2011) Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med 364, 524534.
15. Stewart-Knox, BJ, Bunting, BP, Gilpin, S, et al. (2009) Attitudes toward genetic testing and personalised nutrition in a representative sample of European consumers. Br J Nutr 101, 982989.
16. Rafiq, M, Ianuale, C, Ricciardi, W, et al. (2015) Direct-to-consumer genetic testing: a systematic review of European guidelines, recommendations, and position statements. Genet Test Mol Biomarkers 19, 535547.
17. Stewart, L, Moher, D & Shekelle, P (2012) Why prospective registration of systematic reviews makes sense. Syst Rev 1, 14.
18. Higgins, JPT, Altman, DG, Gøtzsche, PC, et al. (2011) The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343, 19.
19. Persky, S, Kaphingst, KA, Condit, CM, et al. (2007) Assessing hypothetical scenario methodology in genetic susceptibility testing analog studies: a quantitative review. Genet Med 9, 727738.
20. Higgins, J & Green, S (editors) (2011) Cochrane Handbook for Systematic Reviews of Interventions, Version 5.1.0. Cochrane Collaboration, 2011. (accessed January 2015).
21. Wang, C, Gordon, ES, Stack, CB, et al. (2014) A randomized trial of the clinical utility of genetic testing for obesity: design and implementation considerations. Clin Trials 11, 102113.
22. Celis-Morales, C, Livingstone, KM, Marsaux, CFM, et al. (2015) Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries. Genes Nutr 10, 450.
23. Voils, CI, Coffman, CJ, Grubber, JM, et al. (2015) Does type 2 diabetes genetic testing and counseling reduce modifiable risk factors? A randomized controlled trial of veterans. J Gen Intern Med 30, 15911598.
24. Hietaranta-Luoma, H-L, Tahvonen, R, Iso-Touru, T, et al. (2014) An intervention study of individual, apoe genotype-based dietary and physical-activity advice: impact on health behavior. J Nutrigenet Nutrigenomics 7, 161174.
25. Grant, RW, O’Brien, KE, Waxler, JL, et al. (2013) Personalized genetic risk counseling to motivate diabetes prevention: a randomized control trail. Diabetes Care 36, 1319.
26. Marteau, T, Senior, V, Humphries, SE, et al. (2004) Psychological impact of genetic testing for familial hypercholesterolemia within a previously aware population: a randomized controlled trial. Am J Med Genet A 128A, 285293.
27. Frosch, DL, Mello, P & Lerman, C (2005) Behavioral consequences of testing for obesity risk. Cancer Epidemiol Biomarkers Prev 14, 14851489.
28. Sanderson, SC, Persky, S & Michie, S (2010) Psychological and behavioral responses to genetic test results indicating increased risk of obesity: does the causal pathway from gene to obesity matter? Public Health Genomics 13, 3447.
29. Meisel, SF, Beeken, RJ, van Jaarsveld, CHM, et al. (2015) Genetic susceptibility testing and readiness to control weight: results from a randomized controlled trial. Obesity 23, 305312.
30. Smerecnik, CMR, Mesters, I, de Vries, NK, et al. (2009) Alerting the general population to genetic risks: the value of health messages communicating the existence of genetic risk factors for public health promotion. Health Psychol 28, 734745.
31. Food4Me (2015) Personalised nutrition: paving a way to better population health. A White Paper from the Food4Me project, Dublin.
32. Dar-Nimrod, I, Cheung, BY, Ruby, MB, et al. (2014) Can merely learning about obesity genes affect eating behavior? Appetite 81, 269276.
33. Vorderstrasse, AA, Cho, A, Voils, CI, et al. (2013) Clinical utility of genetic risk testing in primary care: the example of type 2 diabetes. Pers Med 10, 549563.
34. Hollands, GJ, French, DP, Griffin, SJ, et al. (2016) The impact of communicating genetic risks of disease on risk-reducing health behaviour: systematic review with meta-analysis. BMJ 352, 111.
35. Markowitz, SM, Park, ER, Delahanty, LM, et al. (2011) Perceived impact of diabetes genetic risk testing among patients at high phenotypic risk for type 2 diabetes. Diabetes Care 34, 568573.
36. Gordon, ES, Griffin, G, Wawak, L, et al. (2012) ‘It’s not like judgment day’: public understanding of and reactions to personalized genomic risk information. J Genet Couns 21, 423432.
37. Ostergren, JE, Gornick, MC, Carere, DA, et al. (2015) How well do customers of direct-to-consumer personal genomic testing services comprehend genetic test results? Findings from the impact of personal genomics study. Public Health Genomics 18, 216224.
38. Vassy, JL, O’Brien, KE, Waxler, JL, et al. (2013) Impact of literacy and numeracy on motivation for behavior change after diabetes genetic risk testing. Med Decis Mak 32, 606615.
39. Haga, SB, Barry, W, Mills, R, et al. (2014) Impact of delivery models on understanding genomic risk for type 2 diabetes. Public Health Genomics 17, 95104.
40. Lachance, CR, Erby, LAH, Ford, BM, et al. (2010) Informational content, literacy demands, and usability of websites offering health-related genetic tests directly to consumers. Genet Med 12, 304312.
41. Skirton, H, Goldsmith, L, Jackson, L, et al. (2012) Direct to consumer genetic testing: a systematic review of position statements, policies and recommendations. Clin Genet 82, 210218.
42. Lundahl, BW, Kunz, C, Brownell, C, et al. (2010) A meta-analysis of motivational interviewing: twenty-five years of empirical studies. Res Soc Work Pract 20, 137160.
43. Bloss, CS, Wineinger, NE, Darst, BF, et al. (2013) Impact of direct-to-consumer genomic testing at long term follow-up. J Med Genet 50, 393400.
44. Weinberg, DS, Myers, RE, Keenan, E, et al. (2014) Genetic and environmental risk assessment and colorectal cancer screening in an average-risk population: a randomized trial. Ann Intern Med 161, 537545.
45. Zeevi, D, Korem, T, Zmora, N, et al. (2015) Personalized nutrition by prediction of glycemic responses. Cell 163, 10791094.
46. Arkadianos, I, Valdes, AM, Marinos, E, et al. (2007) Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J 6, 29.
47. Strecher, VJ, DeVellis, BM, Becker, MH, et al. (1986) The role of self-efficacy in achieving health behavior change. Health Educ Q 13, 7392.
48. Hivert, M-F, Vassy, JL & Meigs, JB (2014) Susceptibility to type 2 diabetes mellitus – from genes to prevention. Nat Rev Endocrinol 10, 198205.
49. Talmud, PJ, Hingorani, AD, Cooper, JA, et al. (2010) Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II Prospective Cohort Study. BMJ 340, b4838.
50. Leventhal, H, Diefenbach, M & Leventhal, EA (1992) Illness cognition: using common sense to understand treatment adherence and affect cognition interactions. Cognit Ther Res 16, 143163.
51. Collins, RE, Wright, AJ & Marteau, TM (2011) Impact of communicating personalized genetic risk information on perceived control over the risk: a systematic review. Genet Med 13, 273277.
52. Meisel, SF, Walker, C & Wardle, J (2012) Psychological responses to genetic testing for weight gain: a vignette study. Obesity (Silver Spring) 20, 540546.
53. Gallagher, P, King, H a, Haga, SB, et al. (2015) Patient beliefs and behaviors about genomic risk for type 2 diabetes: implications for prevention. J Health Commun 20, 728735.
54. Willett, WC (2013) Nutritional Epidemiology, 3rd ed. New York, NY: Oxford University Press.
55. Godino, JG, van Sluijs, EMF, Marteau, TM, et al. (2012) Effect of communicating genetic and phenotypic risk for type 2 diabetes in combination with lifestyle advice on objectively measured physical activity: protocol of a randomised controlled trial. BMC Public Health 12, 444.
56. Silarova, B, Lucas, J, Butterworth, AS, et al. (2015) Information and Risk Modification Trial (INFORM): design of a randomised controlled trial of communicating different types of information about coronary heart disease risk, alongside lifestyle advice, to achieve change in health-related behaviour. BMC Public Health 15, 868.
57. Ronteltap, A, Van Trijp, H, Berezowska, A, et al. (2013) Nutrigenomics-based personalised nutritional advice: in search of a business model? Genes Nutr 8, 153163.
58. Ma, RCW (2015) Genetic testing and counseling to reduce diabetic complications. (accessed August 2015).
59. Green, ED, Guyer, MS; National Human Genome Research Institute (2011) Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204213.
60. Collins, J, Bertrand, B, Hayes, V, et al. (2013) The application of genetics and nutritional genomics in practice: an international survey of knowledge, involvement and confidence among dietitians in the US, Australia and the UK. Genes Nutr 8, 523533.
61. Li, SX, Collins, J, Lawson, S, et al. (2014) A preliminary qualitative exploration of dietitians’ engagement with genetics and nutritional genomics: perspectives from international leaders. J Allied Heal 43, 224231.
62. CDC (2010) ACCE model process for evaluating genetic tests. (accessed May 2015).
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