- Research Paper
- Open Access
A randomized trial of genetic information for personalized nutrition
Genes & Nutrition volume 7, pages 559–566 (2012)
Personal genetic information has become increasingly accessible to the public as a result of direct-to-consumer (DTC) genetic tests; however, concerns have been raised over their value and potential risks. We compared the effects of providing genotype-based dietary advice with general recommendations on behavioral outcomes using a randomized controlled study. Participants were men and women from the Toronto Nutrigenomics and Health Study between the ages of 20–35 years (n = 149) who completed a survey to assess their awareness of DTC genetic tests and nutrigenomics, as well as potential motivations for undergoing genetic testing. Participants were then randomized into an intervention (I) or control (C) group and were given either genotype-based personalized dietary advice or general dietary advice, respectively. A second survey was administered to assess the participants’ opinions of the dietary reports they received. A greater proportion of participants in the intervention group agreed that they understood the dietary advice they were given (93% (I) vs. 78% (C); p = 0.009). Participants in the intervention group were more likely to agree that the dietary recommendations they received would be useful when considering their diet (88% (I) vs. 72% (C); p = 0.02) and wanted to know more about the recommendations (95% (I) vs. 76% (C); p < 0.0001). Only 9% of participants in the intervention group reported feeling uneasy about learning their genetic information. These findings suggest that individuals find dietary recommendations based on genetics more understandable and more useful than general dietary advice. Very few feel uneasy about receiving their genetic information that relates to personalized nutrition.
Recent advances in genomics technologies have made the acquisition of personalized genetic information easily obtainable. Direct-to-consumer (DTC) personal genetic tests claim to provide consumers with information about their genetic ancestry, ability to metabolize nutrients and drugs, and risk for developing diseases (Janssens and van Duijn 2010). One class of genetic tests offers personalized dietary advice based on one’s DNA to improve health (Sterling 2008). Nutrigenomics (or nutritional genomics) is the study of the relationship between genes and diet, and is used as an umbrella term for two complimentary approaches: how nutrients affect gene function and how genetic variation affects nutrient response (Cahill and El-Sohemy 2011). The latter is sometimes referred to as nutrigenetics (El-Sohemy 2007) and includes the study of how genetic variations affect food intake and eating behaviors (Eny and El-Sohemy 2010; Garcia-Bailo et al. 2009). The DTC method of marketing facilitates the sales of genetic tests without the involvement of a healthcare professional (Norrgard 2008). These tests are commercially available through the Internet and are largely unregulated, though significant measures are being taken to regulate this emerging market in certain jurisdictions (McGuire et al. 2010). The cost of the different types of genetic tests available can range from approximately $99 to over $2000 USD (Bloss et al. 2011a). DTC genetic testing for disease susceptibility remains controversial, with opponents arguing that the tests possess limited value due to their questionable clinical validity and utility (Burke 2009; Eng and Sharp 2010; Caulfield et al. 2010). Critics note that predicted risks will continue to change as new genetic variants are discovered, and thus any risk estimates for disease based on currently known common variants are premature (Janssens et al. 2011; Mihaescu et al. 2009). Moreover, environmental factors such as diet, smoking, and physical activity can have a far greater impact on risk, but are often not considered when providing estimates of risk. There is also concern that consumers may experience anxiety if provided with the estimates of higher risk for developing certain diseases based on their genes and may seek out potentially unnecessary health interventions (McGuire and Burke 2008). Another criticism of most DTC genetic tests is that the corresponding advice is not genuinely personalized since the lifestyle recommendations are generally the same, regardless of genotype. Despite these criticisms, proponents of DTC genetic tests argue that there is public interest in genomics and that individuals should have access to their own genetic information (Bloss et al. 2011a; Caulfield et al. 2010). In addition, some propose that direct access to genetic information may motivate consumers to adopt lifestyle behavioral changes aimed at reducing the risk of disease development (Bloss et al. 2011a; McBride et al. 2010). Studies have reported different findings of the effects of disclosure of genetic risk information on health-related behaviors (Arkadianos et al. 2007; Chao et al. 2008; Conradt et al. 2009; Lerman et al. 1997; Marteau et al. 2004; McBride et al. 2002; Vernarelli et al. 2010); however, only one study has investigated the impact of DTC genetic testing on behavior and reported no short-term changes in specific dietary or exercise behaviors (Bloss et al. 2011b). A limitation of that study is that the genetic risk scores that were given to the subjects were not specifically linked to a particular lifestyle behavior, and no personalized advice to reduce the risk of developing a health condition was provided. Importantly, there was no control group in the study. A recent survey of readers of the journal Nature shows that 27% of respondents who had their genomes analyzed changed their diet, lifestyle, or medication based on their genetic information, suggesting that genetic information could impact behavior (Maher 2011).
For appropriate recommendations and regulations regarding DTC genetic tests to be made, the public’s knowledge and opinions of these technologies need to be well understood. A number of studies have surveyed the awareness of and attitudes toward DTC genetic tests either among the general public or among healthcare providers (Cherkas et al. 2010; Goddard et al. 2009; Stewart-Knox et al. 2009; Gollust et al. 2011; Kolor et al. 2009; McGuire et al. 2009; Taylor 2011; Goddard et al. 2007). These studies report low awareness of genetic tests among the general public (13–24%) (Cherkas et al. 2010; Goddard et al. 2007, 2009), but higher awareness among healthcare providers (42–44%) (Goddard et al. 2007; Kolor et al. 2009). Studies have reported an interest in genetic testing among the public, with 50–66% of subjects reporting a willingness to undergo testing (Cherkas et al. 2010; McGuire et al. 2009; Stewart-Knox et al. 2009). Focus group research has also been conducted to better understand the knowledge and attitudes of consumers and healthcare professionals toward nutrigenomics (Morin 2009; Weir et al. 2010). Most consumers in the focus groups were unfamiliar with the term nutrigenomics and did not relate the term personalized nutrition to an individual’s genetic profile, whereas about half of healthcare professionals were aware of the term nutrigenomics (Morin 2009). After being provided with an explanation of nutrigenomics, consumers felt that a tailored diet could help reduce the risk of disease development, while healthcare professionals expressed more skepticism (Morin 2009). While these studies provide valuable insight into the public’s perceptions of nutrigenomics and genetic testing, they have all been either observational or qualitative in design. In addition, there has been some concern that genetic information obtained from a DTC genetic test is not always understood (Leighton et al. 2011), and no studies have examined whether DTC genetic tests that provide personalized nutrition advice are understandable. The objectives of the present study were to conduct a randomized controlled trial to assess behavioral outcomes as well as the awareness, perceptions, and understanding of nutrigenomics and genetic testing.
Materials and methods
Study design and participants
The present study is a randomized controlled trial with a 2:1 ratio of participants in the intervention versus control group. Ethics approval was obtained from the University of Toronto Institutional Review Board, and the study was registered with http://www.clinicaltrials.gov (NCT 01353014). Recruitment was carried out from May to August 2011. Participants provided informed consent by mail and then completed a baseline survey designed to assess the awareness and opinions of genetic testing and nutrigenomics using 4- and 5-point Likert scales. After the baseline survey was completed, participants were randomized to an intervention (I) or control (C) group using Random Allocation Software.
Participants were recruited from the Toronto Nutrigenomics and Health Study (TNHS, n = 1,639), which is a cross-sectional study examining the role of genetics in food intake and food selection as well as gene–diet interactions on the biomarkers of chronic disease in young men and women between the ages of 20–29 years at the time of recruitment. The TNHS cohort is multi-ethnic, with participants representing three major ethnic groups: Caucasian, East Asian, and South Asian (Table 1). Recruitment for the TNHS study was carried out at the University of Toronto from 2004 to 2010. Participants provided a blood sample, and genotyping was performed for several single nucleotide polymorphisms (SNPs) involved in nutrient response and metabolism. A subset of the TNHS cohort (n = 354) was contacted by e-mail or phone to participate in the present study (Fig. 1). Since the recommendations in this study were based on caffeine, vitamin C, sugar, and sodium, eligible participants were those who consumed at least 100 mg of caffeine per day, 10% of total energy from added sugars per day, and 1,500 mg of sodium per day and did not take vitamin C-containing supplements. Three e-mail attempts were made, and if no response was received, one phone call was made. Eligible women who were pregnant or breast-feeding at the time of recruitment were excluded from the study.
Participants in the intervention group (n = 92) were e-mailed a personalized dietary report providing recommendations for daily intakes of caffeine, vitamin C, sugar, and sodium based on genotypes for CYP1A2 (Cornelis et al. 2006; Palatini et al. 2009), GSTM1 and GSTT1 (Cahill et al. 2009; Horska et al. 2010), TAS1R2 (Eny et al. 2010), and ACE (Poch et al. 2001), respectively. The reports were developed in collaboration with Nutrigenomix Inc. (Toronto, Canada), which is a company that is developing a nutrigenetics test kit for registered dietitians. The reports provided participants with their genotype for each gene, an explanation of what the genotype means in terms of the dietary component and a personalized recommendation for daily intake of the dietary component (Table 2). Participants in the control group (n = 46) received general dietary recommendations from health organizations for the same dietary components without genetic information (Table 2). After participants read the dietary report, a post-intervention survey was completed to assess their opinions of the advice they were given.
Surveys were created using the online survey site SurveyMonkey (http://www.surveymonkey.com). Questions included were based on a literature review as well as issues raised in the Harvard University Personal Genetics Education Project (Personal genetics education project 2010). The baseline survey asked how much participants heard about DTC genetic testing to assess their awareness (Table 3). Participants were also asked how much they knew about nutrigenomics. Survey statements such as “I would take a genetic test to learn more about myself” were used to assess participants’ motivations to undergo genetic testing (Table 4). The post-intervention survey consisted of statements such as “The dietary recommendations will be useful when I consider my diet” to assess the participants’ opinions of the value of the dietary recommendations (Fig. 2).
Statistical analyses were performed using the Statistical Analysis Software (version 9.2; SAS Institute Inc., Cary, NC). Participants who reported “strongly agree” or “somewhat agree” to statements on the post-intervention survey were grouped (“agree”), and the Chi-square test was used to compare the frequency of “agree” to all other responses (“strongly disagree,” “somewhat disagree,” and “neither agree nor disagree”). Fisher’s exact test was used when a response category consisted of fewer than 5 counts. Significant p values are two-sided and less than 0.05.
Response rate and description of participants
Of the 157 participants who were sent the baseline survey, 149 participants completed the survey giving a response rate of 95% (Fig. 1). The mean ± standard deviation age of the participants was 25 ± 4 years old and 76% were female (Table 1). The participants were highly educated with 87% having a university or college degree. Of the 149 participants who completed the baseline survey, 138 were randomized into an intervention or control group. The remaining 11 participants did not respond to subsequent e-mail attempts. There were no significant differences between the characteristics of participants in the control or intervention group (Table 1).
Approximately half of the participants (52%) reported having heard “nothing” about DTC genetic testing, while 18% reported hearing “a fair amount” or “a lot”. A smaller proportion of participants reported knowing “nothing” about nutrigenomics (30%), with just over half reporting that they knew “a little bit” about the science (52%) (Table 3). Interest in the relationship between diet and genetics was high, with 90% of participants reporting either “strongly agree” or “somewhat agree” to the survey statement. The majority of participants (87%) also agreed that they would benefit from learning about how their genetic makeup would affect their diet. Consistent with this, 75% of participants agreed that learning about their genetic makeup would affect what they ate. The greatest motivators participants reported for undergoing genetic testing were to learn more about themselves and to encourage themselves to adopt a healthier lifestyle (86 and 83%, respectively), while 73% of participants agreed that they would take a genetic test to have their doctor monitor their health more closely. Only 7% of participants strongly agreed that they would be uncomfortable learning about their genetic makeup (Table 4).
After receiving the dietary report, a greater proportion of participants in the intervention group agreed that they understood the dietary advice they received (93% (I) vs. 78% (C); p = 0.009). As expected, more participants in the intervention group agreed that the recommendations they received were new to them (86% (I) vs. 28% (C); p < 0.0001). Participants in the intervention group were also more likely to agree that they enjoyed learning about the recommendations (96% (I) vs. 72% (C); p < 0.0001), and only 9% agreed that they felt uneasy learning about their genetics (of which only one person reported “strongly agree”). In addition, participants in the intervention group were more likely to agree that the recommendations would be useful when considering their diet (88% (I) vs. 72% (C); p = 0.02) and that they would like to know more about the dietary recommendations they were given (95% (I) vs. 76% (C); p < 0.0001) (Fig. 2).
The results of the present study demonstrate that individuals are interested in nutrigenomics and report finding dietary recommendations based on genetics more useful than general dietary recommendations. Although concern exists over the potential for genetic information to induce anxiety in some individuals, very few participants in the intervention group agreed that they felt uneasy learning about their genetic information. Rather, 96% of participants who received their genetic information agreed that they enjoyed learning about their genetic information and dietary recommendations. This finding suggests that providing this kind of information is not likely to induce anxiety and that young adults may embrace a new era of personalized nutrition that could emerge through the advancement of personalized genomics. However, the nature of the genetic information that was provided in this study might have been perceived as less serious than genetic information related to disease risk. Although the participants in this study had an awareness of the science of nutrigenomics, only 18% reported an awareness of DTC genetic testing while 52% reported no awareness. This finding is consistent with previous surveys of the general public conducted in the UK and US (Cherkas et al. 2010; Goddard et al. 2007, 2009; Kolor et al. 2009). Despite the considerable attention DTC genetic testing has received in recent years (Lynch et al. 2011), this finding suggests that media coverage of DTC genetic testing has not yet greatly impacted young adults.
Scientific literacy and communication of genetic information are important issues to consider when studying the societal impact of DTC genetic testing (McBride et al. 2010). The literacy demands and quality of informational content across DTC genetic testing Web sites have been shown to vary (Lachance et al. 2010), and there is concern that consumers may misinterpret or not understand DTC genetic test results (Leighton et al. 2011). In the present study, a greater proportion of participants in the intervention group agreed that they understood the dietary report they were given, suggesting that dietary recommendations based on genetics can be more understandable than general dietary recommendations. This implies that providing individuals with clear, personalized nutritional advice may result in greater understanding. An important strength of the present study is the use of a randomized controlled trial, which eliminates the possibility of confounding and allows for direct comparisons to be made between experimental groups.
In considering the results of this study, some limitations should be noted. In the present study, no in-person contact was made with study participants, potentially affecting the reliability of the results. However, DTC genetic testing can be completed without in-person contact, so the nature of this study closely mimics the nature of DTC genetic testing. Seventy-six percent of participants in this study were females, and this affected our ability to report any sex-specific findings. However, excluding the males did not materially alter any of the results, suggesting that there were no major differences between men and women in this population. The age of participants in the current study was between 20 and 35 years, so findings might not be the representative of other age groups. In addition, the participants were highly educated and previously participated in a nutrigenomics study. This could explain the high degree of reported understanding of the gene-based dietary recommendations, although participants in the control group reported less understanding of the general dietary recommendations, yet were equally educated.
This study is the first to compare the impact of genotype-based personalized dietary advice with general dietary recommendations. Dietary recommendations based on genotype were reported to be more understandable than general dietary recommendations and were also reported to be more useful. Participants reported that they would not be uncomfortable learning about their own genetic information. Consistent with this, participants in the intervention group did not express discomfort in learning about their genetics and were more likely to report enjoyment in learning about the dietary recommendations they were given, as well as a greater desire to know more about the recommendations. Direct-to-consumer genetic tests based on personalized nutrition might, therefore, be more valuable that those based solely on disease risk predictions.
Arkadianos I, Valdes AM, Marinos E, Florou A, Gill RD, Grimaldi KA (2007) Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J 6:29
Bloss CS, Darst BF, Topol EJ, Schork NJ (2011a) Direct-to-consumer personalized genomic testing. Hum Mol Genet
Bloss CS, Schork NJ, Topol EJ (2011b) Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med 364(6):524–534. doi:10.1056/NEJMoa1011893
Burke W (2009) Clinical validity and clinical utility of genetic tests. Curr Protoc Hum Genet Chapter 9:Unit 9 15. doi:10.1002/0471142905.hg0915s60
Cahill LE, El-Sohemy A (2011) Nutrigenomics: a possible road to personalized nutrition. In: Moo-Young M (ed) Comprehensive biotechnology, vol 4, 2nd edn. Elsevier, Amsterdam, pp 703–712
Cahill LE, Fontaine-Bisson B, El-Sohemy A (2009) Functional genetic variants of glutathione S-transferase protect against serum ascorbic acid deficiency. Am J Clin Nutr 90(5):1411–1417
Caulfield T, Ries NM, Ray PN, Shuman C, Wilson B (2010) Direct-to-consumer genetic testing: good, bad or benign? Clin Genet 9(6–7):48–50
Chao S, Roberts JS, Marteau TM, Silliman R, Cupples LA, Green RC (2008) Health behavior changes after genetic risk assessment for Alzheimer disease: The REVEAL Study. Alzheimer Dis Assoc Disord 22(1):94–97. doi:10.1097/WAD.0b013e31815a9dcc
Cherkas LF, Harris JM, Levinson E, Spector TD, Prainsack B (2010) A survey of UK public interest in internet-based personal genome testing. PLoS ONE 5(10):e13473. doi:10.1371/journal.pone.0013473
Conradt M, Dierk JM, Schlumberger P, Albohn C, Rauh E, Hinney A, Hebebrand J, Rief W (2009) A consultation with genetic information about obesity decreases self-blame about eating and leads to realistic weight loss goals in obese individuals. J Psychosom Res 66(4):287–295. doi:10.1016/j.jpsychores.2008.09.003
Cornelis MC, El-Sohemy A, Kabagambe EK, Campos H (2006) Coffee, CYP1A2 genotype, and risk of myocardial infarction. JAMA 295(10):1135–1141
El-Sohemy A (2007) Nutrigenetics. Forum Nutr 60:25–30. doi:10.1159/0000107064
Eng C, Sharp RR (2010) Bioethical and clinical dilemmas of direct-to-consumer personal genomic testing: the problem of misattributed equivalence. Sci Transl Med 2 (17):17cm15
Eny KM, El-Sohemy A (2010) The Genetic Determinants of Ingestive Behavior: sensory, energy homeostasis and food reward aspects of ingestive behavior. In: Dube L, Bechara AD, Drewnowski A, LeBel J, James P, Yada RY, Laflamme-Sanders M-C (eds) Obesity prevention: the role of brain and society on individual behavior. Elsevier, pp 149–160
Eny KM, Wolever TM, Corey PN, El-Sohemy A (2010) Genetic variation in TAS1R2 (Ile191Val) is associated with consumption of sugars in overweight and obese individuals in 2 distinct populations. Am J Clin Nutr 92(6):1501–1510
Garcia-Bailo B, Toguri C, Eny KM, El-Sohemy A (2009) Genetic variation in taste and its influence on food selection. OMICS 13(1):69–80. doi:10.1089/omi.2008.0031
Goddard KA, Moore C, Ottman D, Szegda KL, Bradley L, Khoury MJ (2007) Awareness and use of direct-to-consumer nutrigenomic tests, United States, 2006. Genet Med 9(8):510–517. doi:10.1097/GIM.0b013e31812e6ac3
Goddard KA, Duquette D, Zlot A, Johnson J, Annis-Emeott A, Lee PW, Bland MP, Edwards KL, Oehlke K, Giles RT, Rafferty A, Cook ML, Khoury MJ (2009) Public awareness and use of direct-to-consumer genetic tests: results from 3 state population-based surveys, 2006. Am J Public Health 99(3):442–445. doi:AJPH.2007.131631
Gollust SE, Gordon ES, Zayac C, Griffin G, Christman MF, Pyeritz RE, Wawak L, Bernhardt BA (2011) Motivations and perceptions of early adopters of personalized genomics: perspectives from research participants. Public Health Genom. doi:10.1159/000327296
Horska A, Mislanova C, Bonassi S, Ceppi M, Volkovova K, Dusinska M (2010) Vitamin C levels in blood are influenced by polymorphisms in glutathione S-transferases. Eur J Nutr. doi:10.1007/s00394-010-0147-2
Janssens AC, van Duijn CM (2010) An epidemiological perspective on the future of direct-to-consumer personal genome testing. Investig Genet 1(1):10. doi:10.1186/2041-2223-1-10
Janssens AC, Wilde AA, van Langen IM (2011) The sense and nonsense of direct-to-consumer genetic testing for cardiovascular disease. Neth Heart J 19(2):85–88. doi:10.1007/s12471-010-0069-x
Kolor K, Liu T, St Pierre J, Khoury MJ (2009) Health care provider and consumer awareness, perceptions, and use of direct-to-consumer personal genomic tests, United States, 2008. Genet Med 11(8):595. doi:10.1097/GIM.0b013e3181b1cc2c
Lachance CR, Erby LA, Ford BM, Allen VC Jr, Kaphingst KA (2010) Informational content, literacy demands, and usability of websites offering health-related genetic tests directly to consumers. Genet Med 12(5):304–312. doi:10.1097/GIM.0b013e3181dbd8b2
Leighton JW, Valverde K, Bernhardt BA (2011) The general public’s understanding and perception of direct-to-consumer genetic test results. Public Health Genom. doi:10.1159/000327159
Lerman C, Gold K, Audrain J, Lin TH, Boyd NR, Orleans CT, Wilfond B, Louben G, Caporaso N (1997) Incorporating biomarkers of exposure and genetic susceptibility into smoking cessation treatment: effects on smoking-related cognitions, emotions, and behavior change. Health Psychol 16(1):87–99
Lynch J, Parrott A, Hopkin RJ, Myers M (2011) Media coverage of direct-to-consumer genetic testing. J Genet Couns 20(5):486–494. doi:10.1007/s10897-011-9374-9
Maher B (2011) Nature readers flirt with personal genomics. Nature 478:19. doi:10.1038/478019a
Marteau T, Senior V, Humphries SE, Bobrow M, Cranston T, Crook MA, Day L, Fernandez M, Horne R, Iversen A, Jackson Z, Lynas J, Middleton-Price H, Savine R, Sikorski J, Watson M, Weinman J, Wierzbicki AS, Wray R (2004) Psychological impact of genetic testing for familial hypercholesterolemia within a previously aware population: a randomized controlled trial. Am J Med Genet A 128A(3):285–293. doi:10.1002/ajmg.a.30102
McBride CM, Bepler G, Lipkus IM, Lyna P, Samsa G, Albright J, Datta S, Rimer BK (2002) Incorporating genetic susceptibility feedback into a smoking cessation program for African-American smokers with low income. Cancer Epidemiol Biomarkers Prev 11(6):521–528
McBride CM, Koehly LM, Sanderson SC, Kaphingst KA (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 31:89–103. doi:10.1146/annurev.publhealth.012809.103532
McGuire AL, Burke W (2008) An unwelcome side effect of direct-to-consumer personal genome testing: raiding the medical commons. JAMA 300(22):2669–2671. doi:10.1001/jama.2008.803
McGuire AL, Diaz CM, Wang T, Hilsenbeck SG (2009) Social networkers’ attitudes toward direct-to-consumer personal genome testing. Am J Bioeth 9(6–7):3–10. doi:10.1080/15265160902928209
McGuire AL, Evans BJ, Caulfield T, Burke W (2010) Science and regulation. Regulating direct-to-consumer personal genome testing. Science 330(6001):181–182. doi:10.1126/science.1194006
Mihaescu R, van Hoek M, Sijbrands EJ, Uitterlinden AG, Witteman JC, Hofman A, van Duijn CM, Janssens AC (2009) Evaluation of risk prediction updates from commercial genome-wide scans. Genet Med 11(8):588–594. doi:10.1097/GIM.0b013e3181b13a4f
Morin K (2009) Knowledge and attitudes of Canadian consumers and health care professionals regarding nutritional genomics. OMICS 13(1):37–41. doi:10.1089/omi.2008.0047
Norrgard K (2008) DTC genetic testing for diabetes, breast cancer, heart disease and paternity. Nature Educ 1(1)
Palatini P, Ceolotto G, Ragazzo F, Dorigatti F, Saladini F, Papparella I, Mos L, Zanata G, Santonastaso M (2009) CYP1A2 genotype modifies the association between coffee intake and the risk of hypertension. J Hypertens 27(8):1594–1601. doi:10.1097/HJH.0b013e32832ba850
Personal genetics education project (2010) Harvard Medical School Department of Genetics (2010) Ethical considerations and personal genome sequencing. Available: http://genepath.med.harvard.edu/WuLab/pgEd/questions.html. Accessed 15 November 2010
Poch E, Gonzalez D, Giner V, Bragulat E, Coca A, de La Sierra A (2001) Molecular basis of salt sensitivity in human hypertension. Evaluation of renin-angiotensin-aldosterone system gene polymorphisms. Hypertension 38(5):1204–1209
Sterling R (2008) The on-line promotion and sale of nutrigenomic services. Genet Med 10(11):784–796. doi:10.1097/GIM.0b013e31818c0441
Stewart-Knox BJ, Bunting BP, Gilpin S, Parr HJ, Pinhao S, Strain JJ, de Almeida MD, Gibney M (2009) Attitudes toward genetic testing and personalised nutrition in a representative sample of European consumers. Br J Nutr 101(7):982–989. doi:10.1017/S0007114508055657
Taylor S (2011) A population-based survey in Australia of men’s and women’s perceptions of genetic risk and predictive genetic testing and implications for primary care. Public Health Genom. doi:10.1159/000324706
Vernarelli JA, Roberts JS, Hiraki S, Chen CA, Cupples LA, Green RC (2010) Effect of Alzheimer disease genetic risk disclosure on dietary supplement use. Am J Clin Nutr 91(5):1402–1407. doi:10.3945/ajcn.2009.28981
Weir M, Morin K, Ries N, Castle D (2010) Canadian health care professionals’ knowledge, attitudes and perceptions of nutritional genomics. Br J Nutr 1–8. doi:10.1017/s0007114510002035
This study was supported by a grant from the Advanced Foods and Materials Network (AFMNet). Ahmed El-Sohemy holds a Canada Research Chair in Nutrigenomics. The authors would like to thank Maria Tassone for her assistance with subject recruitment and data collection.
Conflict of interest
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
About this article
Cite this article
Nielsen, D.E., El-Sohemy, A. A randomized trial of genetic information for personalized nutrition. Genes Nutr 7, 559–566 (2012). https://doi.org/10.1007/s12263-012-0290-x
- Personalized nutrition
- Genetic testing